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100 Inspiring Statistics Research Topics

Definition and importance of statistics.

The definition of statistics is always different, depending on your subject and methodology. In simple terms, it is a defined study, analysis, and manipulation of data that must be reviewed. The complex part of statistical analysis is drawing conclusions or coming up with reports. Since it all comes down to data interpretation, one must think about choosing good statistics research topics. Start by addressing various scientific, industrial, or social problems. It will make it easier to narrow things down and find the most efficient solution, be it a manual statistical interpretation or software automation. If things do not work for you, remember that you can pay for research paper and receive additional help with calculation or methodology choice. It will also help you to quote every statistical bit of data correctly if it has been taken from an outside source! 

Statistics Research Topics

How To Write Statistics Research Topics?

The trick here is to know what methodology will be used to collect and interpret statistical data. Even if you have not chosen your statistics project topic, think about it before going any further. It will help you learn about what kind of data will be researched as the sample will be picked correctly. Your basic outline for choosing the right topic should be this way:

  • Introduction of a problem or a scenario. 
  • Methodology choice and explanation. 
  • Statistical research itself in the body part. 
  • Samples variables and deviations. 
  • Statistical interpretation as your conclusion part. 

Always provide sources for statistical data that has been referred to if it is not your first-hand obtained data! 

100 Research Topics For Statistics 

- good statistics research topics .

It must be noted that statistics are required by numerous disciplines these days, which is why choosing something good for your research can deal with anything. Starting with social media analysis to an estimation of students that have passed the exam successfully, all of it can relate to good stat research topics. 

  • The pros and cons of regression analysis. 
  • How accurate is the use of AI-based tools in statistical analysis?
  • The online news reports and the fluctuations: statistical reports. 
  • How can statistical discrepancy be fixed with the help of estimation methods? 
  • National income and the regulation of cryptocurrencies. 
  • Covid-19 vaccination in the United States and effectiveness of treatments statistics. 
  • Mathematical prediction models vs observation strategies. 
  • Bias in quantitative data analysis in Sociology studies. 
  • Descriptive statistics vs inferential analysis methods.
  • Artificial sampling and the role of estimation in modeled statistics. 

- Statistics Topics For Research Project 

When you have to choose a specific topic that will reflect statistical methods, start by narrowing things down or determining what kind of analysis will be used. You can think about the role that statistical analysis plays in a certain field. See some examples below: 

  • Social media and sample size determination methods. 
  • SAS and coefficient of variation mistakes: manual vs automatic calculation. 
  • Interpretation of statistical results when using SPSS reporting system.  
  • The role of standard deviation when using Z-test analysis for social subjects. 
  • The reasons for using ANOVA testing when dealing with online surveys. 
  • Commercial hypothesis approach to online trading: why statistical analysis won't be accurate. 
  • How can diversity be explained with the help of statistical analysis?  
  • The link between playing video games and the violent outbreaks among college students: statistics. 
  • College loans: the reasons why the numbers increase and the related controversy. 
  • Analysis of the Markov Chain for determination of statistical limitations. 

- Ph.D. Research Topics In Statistics 

As a rule, dealing with Ph.D. is supposed to be more challenging, yet statistical methods still remain the same. It is the subject and the data sample set that go through the changes. For example, you can choose electrical engineering for your statistical method to match the Ph.D. academic level. 

  • The use of statistical analysis in Quantum Physics: pros and cons when accuracy is essential. 
  • How can numerical calculations help with theoretical polymers. 
  • Data Assimilation when dealing with Big Data processes: statistical variables. 
  • The role of probability when one must apply Stochastic Analysis methods. 
  • Applicable Analysis and the benefits of statistical surveys for social distancing. 
  • Improvement of prediction methods in aeronautics. 
  • Cryptocurrencies and the statistical methods for SWIFT transactions. 
  • Why acceleration in the banking sector can be more harmful than useful today? 
  • Cell response and genetics: manual analysis and temporal factor. 
  • Business intelligence analysis methods: human analysis vs automatic computation. 

- Easy Statistics Research Topics 

If you want to find an easy statistics research area, think about the real-life application of statistics. It will help you choose easier research topics for statistics. Just make sure to provide explanations of how statistical research helps, talk about why it happens, and how exactly. 

  • The role of statistics in Data Mining processes. 
  • The real-life use of entropy estimation in engineering fields. 
  • Statistical analysis in the field of speech recognition. 
  • Online grammar checking and the use of empirical entropy methods. 
  • Ranking statistical approach when estimating the accuracy of college exams. 
  • Estimation of probiotics: how much time is necessary for an accurate statistical sample? 
  • How will the population of the United States increase in twenty years? 
  • What statistical methods are most useful for active sports? 
  • The legislation and the statistical reports dealing with controversial subjects. 
  • Transparency of statistical methods and the U.S. Census Bureau reporting system. 

- Survey Methods Statistics Research Topics 

Not a single statistical method may go without some sort of survey method. It is one of the reasons why we have included various statistics research paper topics that are based on surveys and their practical examples. 

  • The pros and cons of online surveys for business purposes. 
  • Data collecting and the use of the predefined groups. 
  • Analysis of strengths of multiple-question survey method: Geographical dependence and statistical survey methods
  • Sampling accuracy and the link to wording in survey questions. 
  • How can statistical studies become more cost-effective by turning to surveys? 
  • Drawing survey conclusions and imputation techniques. 
  • The challenges of super population models in healthcare researching. 
  • What has the Covid-19 pandemic revealed about inaccurate surveys in 2021? 
  • Differential calculus versus spatial statistical research methods. 
  • Inner calculation formulas are most commonly used in online surveys. 

- Business Statistics Research Topics 

When you need something statistical for your business, think about estimation, prognosis, and analysis. In the majority of cases, you shall deal with economics and finances to provide the pros and cons of certain methodologies. See some statistical research topic examples that relate to business matters: 

  • Economic data analysis when dealing with probabilities. 
  • Data distribution when working with descriptive samples: violations, bias, and privacy matters. 
  • Inferential statistics for small business owners: things one must know. 
  • The peculiarities of business data sampling: pros and cons of software solutions. 
  • Linear regression analysis: how can two different business projects be approached at once? 
  • Index numbers, random probability, and accuracy in economic data relations. 
  • Programming statistics: the benefits of dataset approach to statistics. 
  • Commercial statistics: how should the information be prepared for the best accuracy? 
  • Suggestive evidence vs real evidence among business corporations. 
  • AI-based statistical report analysis: financial calculation vs human estimation methods. 

- Applied Statistics Research Topics 

If your college professor asked you to deal with applied statistics for your next assignment, have no worries because applied statistics are related to practice. For example, you can provide a certain case or turn to an actual event where statistical practice can or will be used. Once you choose a case study, narrow things down and see our examples: 

  • The challenges of statistical analysis and unstructured data. 
  • The pros and cons of text mining methods and educational statistics. 
  • Scientific approach to analysis vs basic social media analysis methods. 
  • Statistical software: what kind of data should not pass through classic solutions? 
  • The field of healthcare and processing of sensitive statistical information. 
  • Energy sources and statistical estimation of the green energy benefits. 
  • Changes in politics and turbulence in economic estimation. 
  • Globalization and statistical information coming from more than one source. 
  • Composition of a manual statistical report for unstable political environments. 
  • The percentage of aggression in active sports: behavioral statistics. 

- Sports Statistics Research Topics 

Regardless of whether you are making bets on sports or want to find out who might win the game by turning to scientific methods, sports statistics is what you will need. See some good statistics research topics about sports to get inspired: 

  • Why are Pareto charts preferred more compared to bubble charts for baseball series? 
  • Data analysis in sporting events: a comparison of baseball and basketball statistics.
  • Recording live data sets: how can median accuracy be achieved? 
  • The role of free agencies for ranking purposes: bias in sports statistics.
  • The peculiarities of NFL statistician's work and the reporting privacy. 
  • Why does the average distance gained per running session matter in soccer? 
  • Past games analysis information vs future estimates. 
  • The role of the news organization and the live changes to statistical estimation. 
  • Inter-sportive data and the commonalities encountered by statisticians in NBA and NFL. 
  • Knowledge of strengths and weaknesses of teams and their importance for accurate statistics in sports. 

- Possible Research Topics For Statistics 

Here are the possible subjects where statistics can be researched. If you are not good with formulas and mathematical calculations, these inspiring statistical research topics will help you: 

  • Cyberbullying and common patterns used for online attacks: collecting stats. 
  • The role of statistics during political campaigns and elections. 
  • The success ratio of male vs female employees in Amazon Inc. 
  • The role of sociological estimation for the establishment of statistical data sets. 
  • How can data mining practices help establish more accurate statistics? 
  • The role of urgent calculation for statistical reports in the military. 
  • Estimation of descriptive statistics during Covid-19 times. 
  • Western and Southern African statistical reporting agencies: case study comparison. 
  • Statistical bias in politics: why transparency cannot be achieved. 
  •  The use of statistical estimation in mechanical engineering. 

- Psychology Research Topics for Statistics 

Psychology, Sociology, Healthcare, and Education are among those topics where statistical analysis is essential. Since psychology encompasses philosophy and the constant variables, depending on the case, these statistical psychology topics will be helpful. 

  • PTSD and understanding of descriptive trends in Psychology. 
  • Statistical testing and probability's importance: the role of the researchers. 
  • Acceptable significance and probability levels in clinical Psychology. 
  • The use of hypothesis and the role of P 0.05 for better comprehension. 
  • What statistical data is usually rejected in Psychology? 
  • The use of elementary statistical principles and reasoning in psychological analysis. 
  • The role of correlation when there are several psychological concepts at play. 
  • Statistical reporting based on actual case study learning and modeling. 
  • Naturalistic observation as a scientific sample in Psychology. 
  • How should descriptive statistics capture behavioral data sets? 

Fitting Your Statistical Research Correctly 

One of the most common challenges with statistics is knowing where to fit related data without making it look awkward. If you are in such a situation, our friendly experts are here to provide assistance. As they write research papers for money , they provide you with custom-tailored ideas and will help you avoid plagiarism as you refer to existing case studies. If you want to end up with a great statistical research project, asking for additional guidance is only natural! Take your time to research our list of inspiring statistical topics for research paper and get help when necessary! 

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Qualitative & Quantitative data analysis

Best Statistics Research Topics & Ideas For 2021-22

Date published October 7 2021 by Jacob Miller

Statistics is a demanding subject that deals with the collection, analysis, interpretation, evaluation, and management of numeric data. The topic selection of the statistics dissertation can involve the subfields of statistics, i.e. Probability Theory, Mathematical Statistics, Design of Experiments, Sampling, Classification, and Time Series.

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Complications in statistics researches:

This subject is much complicated, further, the implication of the proportions in large quantities under complex theories contribute to the difficulties concerning the subject. That’s why it is hard to find considerable statistics dissertation topics. Moreover, the multiple dimensions of the subject make it more problematic to come up with a focused and comprehensive topic.

Why Choosing a Statistics Dissertation Topic is Hard for Students?

While selecting a topic for a statistics dissertation, you must consider the fundamental idea of statistics, i.e. variation and uncertainty. Certain statistical frameworks and methods are applied to get the results.

The topic of the statistics dissertation should be so close to the subject that you will be able the statistical method in the dissertation and presentation of findings.

There are several reasons which together make it a difficult task for the students to select a worthwhile topic for their statistics dissertation.

Shortage of Ideas

Students usually lack in generating potential ideas concerning different areas and aspects of the subject. That’s why they face difficulty in listing out the suitable statistics topics for the dissertation.

Wider Scope

Statistics has a wide scope. It holds a relation with scientific, industrial, and social problems. So, a dissertation topic for this subject can never stand out alone. Due to this reason, students find it difficult to determine their direction and fail to select a potential topic.

Irrelevant or diversified knowledge

Somehow, if students manage to come up with some understandable topics for their dissertation, the uncertainty of the context or the background leads them towards the confusion. They are unable to find a purpose and the background on which they can base their research.

While this all seems a pretty tough task, so then you may take inspiration from our free dissertation topics, and even better you can get the professional on those each topic.

How Do We Help You Select a Statistics Dissertation Topic?

We have skilled and professional subject experts, who bring the best ideas for your statistics dissertation selection. They are well aware of how to meet your subject requirements and professors’ expectations. Through their expertise, they help you select the most significant topics for your dissertation.

By selecting one of the strong statistics research topics we propose, you may contribute to the subject through your intellectual capabilities and unique ideas. While preparing a list of topic suggestions for you, we focus on the following points.

  • Your level of Education
  • Subject Domain
  • Area of Interest
  • Prerequisite Guidelines by the University (if any)

What do our experts say about the Statistics Topic Selection?

Our statistics dissertation experts are well-equipped with dense knowledge in the subject. They know which topic is worthy to be chosen for your dissertation. According to our experts, your topic must involve data collection, data analysis, and data synthesis.

You also must have to go through with several previous dissertations and research papers regarding the subject so that you can come up with a topic having fine scope, context, relevancy, and accuracy. Further, it should be concise and manageable so that you can complete a dissertation on it within the deadline.

You can avoid all these complexities by hiring our statistics dissertation topic selection services. Our experts have produced hundreds of successful works for the satisfaction of the customers. With vast experience in the world of academics and command of statistics dissertations, they have prepared the list of most suitable statistics dissertation topics.

Bayesian Methods for Functional and Time Series

Kernel regression using the four fourier transform, assessing and accounting for correlation in rna-seq data analysis., a guide to doing statistics in second language research using spss, prediction interval methods for reliability data, relevance of tests of significances uses and limitations., interaction forward selection in ultra-high-dimension functional linear models..

To know the details of the above-mentioned topics and have an idea about their aims and objectives, you can consult with our team. You are welcomed 24/7 to get our consultancy. Further, you can have more potential topics for your statistics dissertation topics by hiring our services.

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List of Best Statistics Research Topics with Objectives


  • To explore all new bayesian methods which are used in statistical analysis.
  • To introduce new methodology of bayesian which are suitable  for functional and time series data.
  • To exhibit the functional challenges provided by the methodology. 

To explore the methods of kernel  regression

To demonstrate  the method  of speeding up the computation of kernel.

To analyse the FFT to improve the computation of kernel.

Difficulties in Learning Basic Concepts in Probability and Statistics: Implications of Research.

To explore the importance of statistics and probability.

To examine the different methods of statistics and probability used in education system. 

To provide the need for collaborative and cross-disciplinary in researches. 

To explore the concepts behind the usage of statistics in different domains.

To examine the concept of statistics in Second Language.

To study and implement the SPSS software in statistics.

To study the importance of Prediction in statistics.

To analyse the statistical Prediction methods in statistics theory.

To examine the different methods of Prediction interval under the parametric framework. 

To study the importance of statistical tools and significance test both in parametric and nonparametric test.

To examine the statistical tools significance in decision making.

To evaluate the statistical significance test in information retrieval.

To study the statistical methods for the variable selection in ultra-high dimensional functional linear models.

To propose two forward selection procedures on the basis of coefficients approximation.

To demonstrate the application of the proposed methodologies.

Bayes Methods for Biclustering and Vector Data with Binary Coordinates.

To explore the different method of Bayes and its applications.

To examine the Bayes method for the purpose of biclustering and inference for mixture models.

To represent the performance of model through the simulation and applications to real datasets.

To study the concept behind the RNA- sequence data analysis and its procedure.

To examine the papers on the analysis of RNA- sequence data analysis.

To perform a simulation and validate the proposed methods on the basis of results.

An Exploration of Techniques Used in Data Analytics to Produce Analysed Data in Graphical Format.

To explore the techniques used in data analytics used for various purposes in order to produce visual charts.

To demonstrate the use of python language as a main feature in Data analytics.

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Watch out for these research topics in Statistics and Big data

Big data is here to stay. But it is no longer a new technology as a lot of firms have already embraced big data; take, for example, Hadoop, which exemplifies an open-source big data project. Think about the following ideas for your next research paper.

  • Harness NoSql and Hadoop to accelerate big data processing.
  • Swift access to data using in-memory concept.
  • Using R programming language for textual data analysis.
  • Statistical analyses of psychological dysfunction on pupil’s academic progress.
  • Interactive and auto-update of R-plot graphs from a webpage without redrawing.
  • Predicting the future using predictive analysis. How big data play a role?
  • Smart big data applications to study the past by way of big data.
  • Using a circular nonparametric method to estimate entropy.
  • Why salaries and expectations of data scientists and data engineers are high?
  • Cybersecurity attacks? Merge Hadoop with SIEM (Security Information and Management) application.
  • Study on the growth of IoT in various industries and sizable impact of IoT on big data.
  • Accelerated Life Testing models: apply stress factors life-testing experiment.
  • Extracting hidden patterns and predicting financial markets thru data mining. Using advanced techniques in statistics.
  • Using survey sampling tools to understand dyslexia in a specific community.
  • Leveraging statistical, computational techniques, neural networks wavelets, and genetic algorithms to solve complex financial issues.
  • Decision theoretic method for getting ranking and selection procedures.
  • Using advanced mathematical concepts of wavelets in econometric modelling.
  • Pharmacodynamics: what are the stochastic models?
  • Natural language processing (NLP) in clinical research: Application of methods to anonymize data.
  • Correlation between employee engagement and employee performance.
  • Analyzing news coverage in politics and identifying patterns.
  • Linear method of the equation: compare and contrast of Gaussian elimination and Cholesky decomposition techniques.
  • Statistical analysis of criminal offenders.
  • Analyzing UK government’s revenue and expenditure.
  • Analyzing the trafficking of children and women and negative effects.
  • An analysis of the benefits of using information technology in bank services to customers.
  • Statistical use of matrices for input and output model and price fixing.
  • A statistical evaluation of the road accident rates on a specific period.
  • A statistical analysis of reported cases of HIV and STD at a particular period of time.
  • A statistical assessment of infant death rates in the state at a certain period of time.
  • Statistical regression analysis on country’s GDP – Europe Vs US.
  • Health impact of asbestos roof panels: a statistical assessment.
  • Statistical study on university pupil’s expenses.
  • Statistical analysis on the impact of pesticides on the microflora of soil classifications.
  • The contrast on fossil fuels and carbon activated from coconuts—a statistical analysis.
  • Critical study on the causes and issues of banking financial distress.
  • An analysis of the merits of using financial reports in evaluating bank’s performance.
  • Solutions for loan defaults in Indian banks: a detailed analysis.

Other research topics may revolve around Bayesian statistics, matching propensity scores, high-dimensional analysis of data, survival data analyses, and, model selections.

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Statistics PhD theses

2015 onwards.

Computational statistics Research Topics for MS PhD

Computational statistics research topic ideas for ms, or ph.d. degree.

I am sharing with you some of the research topics regarding Computational statistics that you can choose for your research proposal for the thesis work of MS, or Ph.D. Degree.

  •  Service data analytics and business intelligence 2017
  • A new computational approach for estimation of the Gini index based on grouped data
  • The odd log-logistic Lindley-G family of distributions: properties, Bayesian and non-Bayesian estimation with applications
  • The 2016 Data Challenge of the American Statistical Association
  • Algorithm for error-free determination of the variance of all contiguous subsequences and fixed-length contiguous subsequences for a sequence of industrial …
  • Zero‐inflated modeling part I: Traditional zero‐inflated count regression models, their applications, and computational tools
  • Spatial Analysis with R: Statistics, Visualization, and Computational Methods
  • Teaching Computational Machine Learning (without Statistics)
  • Preface: International Conference on Mathematics, Computational Sciences and Statistics 2020
  • Reducibility and statistical-computational gaps from secret leakage
  • Mixture cure rate models with neural network estimated nonparametric components
  • GSDAR: a fast Newton algorithm for ℓ 0 regularized generalized linear models with statistical guarantee
  • The unit-improved second-degree Lindley distribution: inference and regression modeling
  • A Bayesian approach to estimate parameters of ordinary differential equation
  • Moment-sos Hierarchy, The: Lectures In Probability, Statistics, Computational Geometry, Control And Nonlinear Pdes
  • Linear models for multivariate repeated measures data with block exchangeable covariance structure
  • R package for statistical inference in dynamical systems using kernel based gradient matching: KGode
  • Use of the heuristic optimization in the parameter estimation of generalized gamma distribution: comparison of GA, DE, PSO and SA methods
  • Feature screening based on distance correlation for ultrahigh-dimensional censored data with covariate measurement error
  • Bi-level variable selection in semiparametric transformation models with right-censored data
  • Contributions to computational Bayesian statistics
  • Semiparametric quantile regression using family of quantile-based asymmetric densities
  • Interpolation of daily rainfall data using censored Bayesian spatially varying model
  • Two generalized nonparametric methods for estimating like densities
  • Comprehensive world university ranking based on ranking aggregation
  • Computational efficiency in continuous (and Discrete!) time models–Comment on Hecht and Zitzmann
  • A new way for ranking functional data with applications in diagnostic test
  • Joint analysis of semicontinuous data with latent variables
  • A Bayesian quantile regression approach to multivariate semi-continuous longitudinal data
  • Optimal imputation of the missing data using multi auxiliary information
  • Comparison among simultaneous confidence regions for nonlinear diffusion models
  • Analysis of multivariate longitudinal data using ARMA Cholesky and hypersphere decompositions
  • Analysis of drowsy driving: exploring subpopulation risk with weighted contingency table tools
  • Variable selection in high-dimensional linear model with possibly asymmetric errors
  • Recent advances in hyperspectral imaging for melanoma detection
  • Estimation and prediction of a generalized mixed-effects model with t-process for longitudinal correlated binary data
  • A time series model based on dependent zero inflated counting series
  • Bayesian inference of nonlinear hysteretic integer-valued GARCH models for disease counts
  • Robust estimation and variable selection in heteroscedastic regression model using least favorable distribution
  • Objective Bayesian analysis for generalized exponential stress–strength model
  • A beyond multiple robust approach for missing response problem
  • Efficient and robust estimation of regression and scale parameters, with outlier detection
  • Goodness-of-fit testing of survival models in the presence of Type–II right censoring
  • Approximate computation of projection depths
  • Topics in computational statistics
  • Genome‐wide prediction of chromatin accessibility based on gene expression
  • Bayesian multiple changepoints detection for Markov jump processes
  • Asymmetric vector moving average models: estimation and testing
  • Modified empirical likelihood-based confidence intervals for data containing many zero observations
  • Modelling dependency effect to extreme value distributions with application to extreme wind speed at Port Elizabeth, South Africa: a frequentist and Bayesian …
  • A scalable Bayesian nonparametric model for large spatio-temporal data
  • A stationary bootstrap test about two mean vectors comparison with somewhat dense differences and fewer sample size than dimension
  • Estimation of parameters of logistic regression for two-stage randomized response technique
  • Latent association graph inference for binary transaction data
  • Spectral clustering-based community detection using graph distance and node attributes
  • Estimation of a CIR process with jumps using a closed form approximation likelihood under a strong approximation of order 1
  • Ultra-high dimensional variable screening via Gram–Schmidt orthogonalization
  • Estimating the number of clusters via a corrected clustering instability
  • Semiparametric model of mean residual life with biased sampling data
  • Computational models of the human visual cortex: on individual differences and ecologically valid input statistics
  • On the empirical estimator of the boundary in inverse first-exit problems
  • A review of approximate Bayesian computation methods via density estimation: Inference for simulator‐models
  • On consistency of the monotone NPMLE of survival function under the mixed case interval-censored model with left truncation
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  • Special Issue on Benchmarking of Computational Intelligence Algorithms in the Applied Soft Computing Journal
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  • Computational Simulation Modeling
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  • Coronary artery heart disease prediction: a comparative study of computational intelligence techniques
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  • Computational Modeling
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  • Review of advanced computational approaches on multiple sclerosis segmentation and classification
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  • Some computational considerations for kernel-based support vector machine
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  • Navigating the Minefield of Computational Toxicology and Informatics: Looking Back and Charting a New Horizon
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  • Computational prediction of protein–protein binding affinities
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  • Computational Framework for Fracture Simulation of Concrete Structures until Failure
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  • A computational intelligence tool for the detection of hypertension using empirical mode decomposition
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  • Understanding Oral Genomics Through Computational Analysis
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  • Drug repurposing for opioid use disorders: integration of computational prediction, clinical corroboration, and mechanism of action analyses
  • EvoEF2: accurate and fast energy function for computational protein design
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  • Submitted to Computational and Structural Biotechnology Journal October 6, 2020
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  • The unstoppable rise of computational linguistics in deep learning
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  • A piRNA regulation landscape in C. elegans and a computational model to predict gene functions
  • Real-time forecasting of epidemic trajectories using computational dynamic ensembles
  • Measuring teacher beliefs about coding and computational thinking
  • Algorithms for heavy-tailed statistics: Regression, covariance estimation, and beyond
  • Improving the computational efficiency of first arrival time uncertainty estimation using a connectivity-based ranking Monte Carlo method
  • Quantum state optimization and computational pathway evaluation for gate-model quantum computers
  • Rothman–Woodroofe symmetry test statistic revisited
  • Robust Wald-type methods for testing equality between two populations regression parameters: A comparative study under the logistic model
  • Seeded Binary Segmentation: A general methodology for fast and optimal change point detection
  • A class of conjugate priors for multinomial probit models which includes the multivariate normal one
  • Computational Modeling of Biofilm Formation and Corrosion Processes on Steel Surfaces
  • Computational Models of Defect Clustering for Tethered Bilayer Membranes
  • Hybrid method based on neural networks and Monte Carlo simulation in view of a tradeoff between accuracy and computational time
  • Computational based on GUI MATLAB for back propagation method in detecting climate change: case study of mataram city
  • Computational methods for detecting cancer hotspots
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  • Computational investigation of geometrical effects in 2D boron nitride nanopores for DNA detection
  • Cardelino: computational integration of somatic clonal substructure and single-cell transcriptomes
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  • A CNN-based computational algorithm for nonlinear image diffusion problem
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  • Inference for a generalised stochastic block model with unknown number of blocks and non-conjugate edge models
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  • The sum of two independent polynomially-modified hyperbolic secant random variables with application in computational finance
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  • Computational flow cytometry of planktonic populations for the evaluation of microbiological-control programs in district cooling plants
  • Deep neural network models for computational histopathology: A survey
  • Computational fluid dynamics for fixed bed reactor design
  • An efficient computational cost reduction strategy for the population-based intelligent optimization of nonlinear dynamical systems
  • Moments of order statistics and k-record values arising from the complementary beta distribution with application
  • Developing computational thinking with a module of solved problems
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  • Scaled Vecchia approximation for fast computer-model emulation
  • Computational electronic structure studies of novel condensed matter phases
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  • Relationships between Computational Thinking Skills, Ways of Thinking and Demographic Variables: A Structural Equation Modeling.
  • Artificial intelligence and computational approaches for epilepsy
  • Computational analysis and verification of molecular genetic targets for glioblastoma
  • Computational simulations reveal the binding dynamics between human ACE2 and the receptor binding domain of SARS-CoV-2 spike protein
  • Pharmacokinetic profiles determine optimal combination treatment schedules in computational models of drug resistance
  • Robust nonparametric regression: A review
  • Tuna locomotion: a computational hydrodynamic analysis of finlet function
  • A Brief History of the Theory and Practice of Computational Literary Criticism (1963-2020)
  • C2STEM: A system for synergistic learning of physics and computational thinking
  • Making Sense of Computational Psychiatry
  • First comprehensive computational analysis of functional consequences of TMPRSS2 SNPs in susceptibility to SARS-CoV-2 among different populations
  • Computational identification of eukaryotic promoters based on cascaded deep capsule neural networks
  • Computational modeling and simulation of ligand-gated ion channels
  • Deep into that darkness peering: a computational analysis of the role of depression in Edgar Allan Poe’s life and death
  • A neuro-computational account of arbitration between choice imitation and goal emulation during human observational learning
  • A computational framework for revealing competitive travel times with low-carbon modes based on smartphone data collection
  • Identification of chymotrypsin-like protease inhibitors of SARS-CoV-2 via integrated computational approach
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  • Computational evaluation of major components from plant essential oils as potent inhibitors of SARS-CoV-2 spike protein
  • Surrogate Modelling and Uncertainty Quantification in Computational Sciences
  • Computational models of drug use and addiction: A review.
  • Computational Based Formulation Design
  • Nonparametric density estimation over complicated domains
  • Computational and numerical simulations for the nonlinear fractional Kolmogorov–Petrovskii–Piskunov (FKPP) equation
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  • Impact of Computational Power on Cryptography
  • Supporting Teachers to Integrate Computational Thinking Equitably
  • Reinforcement Learning on Computational Resource Allocation of Cloud-based Wireless Networks
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  • Conformational energies and equilibria of cyclic dinucleotides in vacuo and in solution: computational chemistry vs. NMR experiments
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  • Computational Analysis of PER2:: LUC Imaging Data
  • Computational Science Laboratory Report
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  • Computational analysis of the SARS-CoV-2 and other viruses based on the Kolmogorov’s complexity and Shannon’s information theories
  • Estimates of Electrical Conductivity from Molecular Dynamics Simulations: How to Invest the Computational Effort
  • Bayesian nonparametric clustering as a community detection problem
  • An Efficient Parallel Hybrid Method of FEM-MLFMA for Electromagnetic Radiation and Scattering Analysis of Separated Objects.
  • Computational and neurocognitive approaches to the political brain: key insights and future avenues for political neuroscience
  • A computational approach for the space-time fractional advection–diffusion equation arising in contaminant transport through porous media
  • CIS and Industry Hand in Hand [President’s Message]
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  • HighResMIP versions of EC-Earth: EC-Earth3P and EC-Earth3P-HR–description, model computational performance and basic validation
  • Reliability and reproducibility in computational science: implementing validation, verification and uncertainty quantification in silico
  • Computational mechanisms of effort and reward decisions in patients with depression and their association with relapse after antidepressant discontinuation
  • Uncertainty Informed Integrated Computational Materials Engineering for Design and Development of Fatigue Critical Alloys
  • Benefits of formalized computational modeling for understanding user behavior in online privacy research
  • Rank-normalization, folding, and localization: An improved ̂R for assessing convergence of MCMC
  • Computational drug repositioning based on multi-similarities bilinear matrix factorization
  • Teaching Metabolism in Upper-Division Undergraduate Biochemistry Courses using Online Computational Systems and Dynamical Models Improves Student …
  • On using computational versus data-driven methods for uncertainty propagation of isotopic uncertainties
  • Computational Approaches for Microbiome Characterization
  • A computational model for soil fertility prediction in ubiquitous agriculture
  • On the computational solution of vector-density based continuum dislocation dynamics models: a comparison of two plastic distortion and stress update algorithms
  • A computational framework for social-media-based business analytics and knowledge creation: empirical studies of CyTraSS
  • Numerical Validation of Computational Fluid Dynamics Simulation of Blood Flow in Cerebral Artery using Discrete Phase Model
  • Compromise design for combination experiment of two drugs
  • Stationarity Statistics on Rolling Windows
  • A general framework and guidelines for benchmarking computational intelligence algorithms applied to forecasting problems derived from an application domain …
  • The W/S test for data having neutrosophic numbers: An application to USA village population
  • The development of computational estimation in the transition from informal to formal mathematics education
  • Chronic mTOR activation induces a degradative smooth muscle cell phenotype
  • Computational resources for identifying and describing proteins driving liquid–liquid phase separation
  • Deep neural networks for computational optical form measurements
  • Design thinking and computational thinking: A dual process model for addressing design problems
  • The Best Laid Plans: Computational Principles of Anterior Cingulate Cortex
  • Comparative analysis of feature selection algorithms for computational personality prediction from social media
  • Computational analysis of fused co-expression networks for the identification of candidate cancer gene biomarkers
  • Computational analysis of entropy generation for cross-nanofluid flow
  • Computational stabilization of T cell receptors allows pairing with antibodies to form bispecifics
  • An invitation to statistics in Wasserstein space
  • How heterogeneity drives tumour growth: a computational study
  • Biological and computational studies for dual cholinesterases inhibitory effect of zerumbone
  • Computational Methods in Heterogeneous Catalysis
  • Inflow Hemodynamics of Intracranial Aneurysms: A Comparison of Computational Fluid Dynamics and 4D Flow Magnetic Resonance Imaging
  • FAIR computational workflows
  • A pilot study of all-computational drug design protocol–from structure prediction to interaction analysis
  • Frequentist delta-variance approximations with mixed-effects models and TMB
  • Surrogate-based computational analysis and design for H-shaped microstrip antenna
  • The project for objective measures using computational psychiatry technology (PROMPT): Rationale, design, and methodology
  • Improving Bayesian statistics understanding in the age of Big Data with the bayesvl R package
  • A Method for the Prediction of Extreme Ship Responses Using Design-Event Theory and Computational Fluid Dynamics.
  • A computational framework of kinematic accuracy reliability analysis for industrial robots
  • Saving computational budget in Bayesian network-based evolutionary algorithms
  • Presentations from Computational Mechanics and Sciences Workshop
  • Variational inference for computational imaging inverse problems
  • Research on BP model optimization of PSO algorithm based on Computational Mathematics
  • Computational Mathematics and Applications
  • Learning Multiple Quantiles with Neural Networks
  • … and transmission of diseases: A novel approach combining audits, calibrated energy models, building performance (BPS) and computational fluid dynamic (CFD) …
  • Computational predictive approaches for interaction and structure of aptamers
  • Computational modeling of excitatory/inhibitory balance impairments in schizophrenia
  • Introduction to Computational Data Science Using ScalaTion
  • Computational design of shape memory polymer nanocomposites
  • A computational approach for printed document forensics using SURF and ORB features
  • NLP Workflows for Computational Social Science: Understanding Triggers of State-Led Mass Killings
  • Computational Advantage from the Quantum Superposition of Multiple Temporal Orders of Photonic Gates
  • Computational tools for modern vaccine development
  • A kernel method for learning constitutive relation in data-driven computational elasticity
  • Connectome 2.0: Cutting-Edge Hardware Ushers in New Opportunities for Computational Diffusion MRI
  • Enhanced Undergraduate Learning through Integration of Theory and Computational Tools.
  • Role of hydrogen bond capacity of solvents in reactions of amines with CO2: A computational study
  • A multiyear investigation of student computational thinking concepts, practices, and perspectives in an after-school computing program
  • An efficient computational scheme for nonlinear time fractional systems of partial differential equations arising in physical sciences
  • Computational Identification of Functional Centers in Complex Proteins: A Step-by-Step Guide With Examples
  • Computational/in silico methods in drug target and lead prediction
  • Computational Prediction of Disordered Protein Motifs Using SLiMSuite
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  • Computational methodologies for optimal sensor placement in structural health monitoring: A review
  • From summary statistics to gene trees: Methods for inferring positive selection
  • Goodness of fit test for almost cyclostationary processes
  • The usage of large data sets in online consumer behaviour: A bibliometric and computational text-mining–driven analysis of previous research
  • Discovering anti-cancer drugs via computational methods
  • Computational Intelligence in the hospitality industry: A systematic literature review and a prospect of challenges
  • Preparing Special Education Preservice Teachers to Teach Computational Thinking and Computer Science in Mathematics
  • Discovering the computational relevance of brain network organization
  • An efficient ADMM algorithm for high dimensional precision matrix estimation via penalized quadratic loss
  • Scalable gradients for stochastic differential equations
  • Rheumatoid arthritis identification using epistasis analysis through computational models
  • Hierarchical Deep Learning Neural Network (HiDeNN): An artificial intelligence (AI) framework for computational science and engineering
  • A new heuristic computational solver for nonlinear singular Thomas–Fermi system using evolutionary optimized cubic splines
  • Computational design of proteins and enzymes
  • Assessing computational thinking abilities among Singapore secondary students: a Rasch model measurement analysis
  • Computational Study of Flow around 2D and 3D Tandem Bluff Bodies
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  • The digital terrain model in the computational modelling of the flow over the Perdigão site: the appropriate grid size
  • Computational models identify several FDA approved or experimental drugs as putative agents against SARS-CoV-2
  • A high-quality cucumber genome assembly enhances computational comparative genomics
  • Computational fluid dynamics simulation of tree effects on pedestrian wind comfort in an urban area
  • Evaluation of different computational methods on 5-methylcytosine sites identification
  • Computational models for active matter
  • Supplementary material: Computational methods and software tools for functional analysis of miRNA data
  • DFT computational insights into structural, electronic and spectroscopic parameters of 2-(2-Hydrazineyl) thiazole derivatives: a concise theoretical and experimental …
  • The development of students’ computational thinking practices in elementary-and middle-school classes using the learning game, Zoombinis
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  • Computational discovery of new 2D materials using deep learning generative models
  • Discovery of Amoebicidal Compounds by Combining Computational and Experimental Approaches
  • Computational identification of putative genes and vital amino acids involved in biennial rhythm in mango (Mangifera indica L.)
  • Computational Framework for Modelling Student Engagement in a University’s Team-Based Learning Ecosystem
  • Computational methods and next-generation sequencing approaches to analyze epigenetics data: profiling of methods and applications
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  • State of the art computational applications in experimental and clinical dentistry
  • Computational drug re-purposing targeting the spike glycoprotein of SARS-CoV-2 as an effective strategy to neutralize COVID-19
  • Skewness in Applied Analysis of Normality
  • Evaluating and improving heritability models using summary statistics
  • Computational modeling and prediction on viscosity of slags by big data mining
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  • Computational modeling and analysis of multi plate clutch
  • Role of Computational Variables on the Performances of COSMO-SAC Model: A Combined Theoretical and Experimental Investigation
  • Identification of novel drug candidates for treating tongue squamous cell carcinoma using computational approaches
  • Computational complexity reduction of neural networks of brain tumor image segmentation by introducing fermi–dirac correction functions
  • Laccase Engineering by Directed and Computational Evolution
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  • Performance and computational complexity analysis of coding tools in AVS3
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  • Computational Metagenomics: State-of-the-Art, Facts and Artifacts
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  • Need for Computational and Psycho-linguistics Models in Natural Language Processing for Web Documents
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  • A computational framework for identifying the transcription factors involved in enhancer-promoter loop formation
  • Paranoid Transformer: Reading Narrative of Madness as Computational Approach to Creativity
  • Computational assessment of MCM2 transcriptional expression and identification of the prognostic biomarker for human breast cancer
  • Equitable approaches: opportunities for computational thinking with emphasis on creative production and connections to community
  • A general robust t-process regression model
  • Three-dimensional computational fluid dynamic analysis of high-speed water-lubricated hydrodynamic journal bearing with groove texture considering turbulence
  • Integration of computational tools, data analysis and social science into food safety risk assessment
  • XPRESSyourself: Enhancing, standardizing, and automating ribosome profiling computational analyses yields improved insight into data
  • Computational study of transcription factor binding sites
  • Computational modelling and systems ergonomics: a system dynamics model of drink driving-related trauma prevention
  • Teaching Computational Thinking in K-9: Tensions at the Intersection of Technology and Pedagogical Knowledge
  • Computational Analysis of Hi-C Data
  • Robust linear regression for high‐dimensional data: An overview
  • A computational approach for modelling context across different application domains
  • The digital effectiveness on economic inequality: a computational approach
  • On-demand generation of as-built infrastructure information models for mechanised Tunnelling from TBM data: A computational design approach
  • Computational foundations of mind, evolution, and society
  • Computational prediction and interpretation of cell-specific replication origin sites from multiple eukaryotes by exploiting stacking framework
  • High-dimensional structure learning of binary pairwise Markov networks: a comparative numerical study
  • Computational Design and Analysis of a Magic Snake
  • On the Bridge Between Computational Results Of New Line Search Technique
  • The state of undergraduate computational science programs
  • miRNAture—Computational Detection of microRNA Candidates
  • Implementation and characterization of a two-dimensional printed circuit dynamic metasurface aperture for computational microwave imaging
  • Non-periodicity of blood flow and its influence on wall shear stress in the carotid artery bifurcation: An in vivo measurement-based computational study
  • Dissecting the Computational Roles of the Social Brain
  • Climbing down Charney’s ladder: machine learning and the post-Dennard era of computational climate science
  • Advances in the computational development of androgen receptor antagonists
  • Use of computational intelligence techniques to predict flooding in places adjacent to the Magdalena River
  • Computational Multispectral Endoscopy
  • Constraining cosmology with big data statistics of cosmological graphs
  • Computational optimisation of screw orientations for improved locking plate fixation of proximal humerus fractures
  • An efficient computational method for estimating failure credibility by combining genetic algorithm and active learning Kriging
  • Estimation of incident dynamic AUC in practice
  • Machine Learning Technology Reveals the Concealed Interactions of Phytohormones on Medicinal Plant In Vitro Organogenesis.
  • Advancing Conflict Research Through Computational Approaches
  • Introduction to Computational and Bioinformatics Tools in Virology
  • Forcing statistics in resolvent analysis: application in minimal turbulent Couette flow
  • Under the hood: using computational psychiatry to make psychological therapies more mechanism-focused
  • Optimization of envelope design for housing in hot climates using a genetic algorithm (GA) computational approach
  • AdamOptimizer for the Optimisation of Use Case Points Estimation
  • Computational advances of tumor marker selection and sample classification in cancer proteomics
  • The dark side of the ‘Moral Machine’and the fallacy of computational ethical decision-making for autonomous vehicles
  • Self-reported symptoms of covid-19 including symptoms most predictive of SARS-CoV-2 infection, are heritable
  • Robust test for dispersion parameter change in discretely observed diffusion processes
  • School of hard knocks: Curriculum analysis for Pommerman with a fixed computational budget
  • Computational modeling and experimental analysis for the diagnosis of cell survival/death for Akt protein
  • Shallow landslide susceptibility mapping: A comparison between classification and regression tree and reduced error pruning tree algorithms
  • A survey on ensemble learning
  • Computational modeling of intraocular drug delivery supplied by porous implants
  • Computational and experimental microstructural characterization of a magnesium WE43 Alloy processed on a commercially available PBF-LB machine
  • Aeroshape design of reusable re-entry vehicles by multidisciplinary optimization and computational fluid dynamics
  • Ultrasound Based Computational Fluid Dynamics Assessment of Brachial Artery Wall Shear Stress in Preeclamptic Pregnancy
  • Computational modelling of the Δ4 and Δ5 adrenal steroidogenic pathways provides insight into hypocortisolism
  • Computational Radiology in Breast Cancer Screening and Diagnosis Using Artificial Intelligence
  • Mathematical Modeling for the Solutions of Equations and Systems of Equations with Applications Vol. 4
  • A combined experimental and computational analysis of failure mechanisms in open-hole cross-ply laminates under flexural loading
  • Computational investigations of gram-negative bacteria phosphopantetheine adenylyltransferase inhibitors using 3D-QSAR, molecular docking and molecular …
  • Computational studies of mycorrhizal protein: GiHsp60 and its interaction with soil organic matter
  • A combined computational and experimental strategy identifies mutations conferring resistance to drugs targeting the BCR-ABL fusion protein
  • Challenges in the Computational Modeling of the Protein Structure—Activity Relationship
  • MD-TSPC4: Computational Method for Predicting the Thermal Stability of I-Motif
  • Numerical simulation of the fluid flow inside the pressurized tube of the CANDU-6 reactor using the Computational Fluid Dynamics and the presence of nanoparticles …
  • The emerging role of computational design in peptide macrocycle drug discovery
  • R-Squared-Bootstrapping for Gegenbauer-Type Long Memory
  • Investigation of bubble velocity profile in the column flotation cell by computational fluid dynamics simulation
  • Correction to: A computational model of a network of initial lymphatics and pre-collectors with permeable interstitium
  • Using Lego Mindstorms Robotics Programming in Enhancing Computational Thinking among Middle School in Saudi Arabia
  • CAUSALdb: a database for disease/trait causal variants identified using summary statistics of genome-wide association studies
  • A computational investigation on how visitation affects the reproduction number in a dengue fever model
  • Could Dermaseptin Analogue be a Competitive Inhibitor for ACE2 Towards Binding with Viral Spike Protein Causing COVID19?: Computational Investigation
  • Causation in Agent-Based Computational Social Science
  • Computational modelling of foot orthosis for midfoot arthritis: a Taguchi approach for design optimization.
  • Beyond two-point statistics: using the minimum spanning tree as a tool for cosmology
  • Characterization of Protein-Membrane Interfaces through a Synergistic Computational-Experimental Approach
  • Bayesian Bi-clustering Methods with Applications in Computational Biology
  • Overcoming the challenges to enhancing experimental plant biology with computational modeling
  • Skewness, kurtosis, and the fifth and sixth order cumulants of net baryon-number distributions from lattice QCD confront high-statistics STAR data
  • Flood Hazard Mapping in Alluvial Fans with Computational Modeling
  • Interoperability and computational framework for simulating open channel hydraulics: application to sensitivity analysis and calibration of Gironde Estuary model
  • Multiapproach computational modelling of tuberculosis: understanding its epidemiological dynamics for improving its control in Nigeria
  • A Scale Mixture Approach to t-Distributed Mixture Regression
  • Estimation and determinants of Chinese banks’ total factor efficiency: a new vision based on unbalanced development of Chinese banks and their overall risk
  • Partial preservation of the inferior turbinate in endoscopic medial maxillectomy: a computational fluid dynamics study
  • Computational cardiovascular analysis with the variational multiscale methods and isogeometric discretization
  • Computational discovery of plant-based inhibitors against human carbonic anhydrase IX and molecular dynamics simulation
  • Computational Array of Ferromagnetic Effect “Ising Model”
  • Pragmatics of Immigration & Identity: Computational Study of Online Discourses
  • LongGF: computational algorithm and software tool for fast and accurate detection of gene fusions by long-read transcriptome sequencing
  • Computational Analysis of Fluid Dynamics in the Transcatheter Aortic Valve Replacement
  • A new computational platform of structural reliability analysis developed by coupling FERUM and OpenSees
  • What does computational fluid dynamics tell us about intracranial aneurysms? A meta-analysis and critical review
  • A comparison of deep machine learning and Monte Carlo methods for facies classification from seismic data
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  • Computational study of zebrafish immune-targeted microarray data for prediction of preventive drug candidates
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PhD in Statistics

Study with leading statisticians at a world-class university

How to Apply

A PhD offers the chance to undertake a substantial piece of supervised work that is worthy of publication and which makes an original contribution to knowledge in a particular field. Our PhD programme is designed to produce professional social scientists, well versed in a range of advanced statistical techniques and methods, in addition to having an in-depth knowledge of your topic of interest. 

The Department of Statistics is one of the world's leading centres of quantitative methods in the social sciences and has long been home to some of the world's most famous and innovative statisticians. Today, the department has an international reputation for the development of statistical methodology that has grown from our long history of active contributions to research and teaching in statistics. 

Our core research areas are:

  • Data science
  • Probability in finance and insurance
  • Social statistics
  • Time series and statistical learning

If you have any questions about our MPhil/PhD Statistics programme, please  email the Research Manager .  

Research environment

The Department of Statistics at LSE is one of the oldest and most distinguished in the UK. It has a rich research portfolio covering core areas of statistical inference and real applications, particularly in the economic, financial and actuarial, social and industrial arenas. The close collaboration with other LSE departments, our London location and strong international partnerships are reflected in the research life of the Department of Statistics through the members of staff, PhD students, postdoctoral research fellows and the thriving visitor and seminar programmes.

Research in the department is concentrated in the following areas and PhD proposals should normally be linked to one of these areas:

Data Science

Research in the data science area is concerned with the development of new machine learning and statistical methods, and their applications. The areas of applications include the design of novel methods for understanding user behaviour, analysis of social data, modelling and inference for information cascades and epidemic processes that arise in social networks and biomedical applications, as well as algorithms for development of next-generation artificial intelligence systems.

Possible areas of research include:

  • Bayesian inference and predictions
  • Functional data analysis
  • High-dimensional statistics
  • Machine and statistical learning for relational data
  • Network data models, inference and predictions
  • Optimisation and machine learning
  • Reinforcement learning
  • Statistical learning methods in precision medicine
  • Statistical models and inference for ranking data
  • Stochastic models of epidemic processes
  • Stochastic optimisation methods
  • Stochastic processes in econometrics and finance

For more information about potential supervisors and their areas of interest, visit the Data Science research group .

Probability in Finance and Insurance

PhD research in probability in finance and insurance encompasses many aspects of the discipline. Methodological and theoretical research is mainly guided by applications with the aid of both academic and industrial experts, covering topics of modern stochastic finance with an emphasis on insurance and financial mathematics.  Applications include pricing and hedging exotic products, counterparty risk, portfolio optimisation, risk management and insurance, risk transfer and securitisation, etc. 

Research topics may be identified in advance by the applicant or may be arrived at through communication with a potential supervisor. The relative emphasis on methodology/theory vs. application may vary. 

Suggested research areas of PhD research projects include:

  • Energy markets
  • Excursions of Lévy processes and applications in finance and insurance
  • Financial market with frictions
  • Information asymmetry
  • Interface between insurance and finance
  • Lévy processes
  • Optimal stopping
  • Point processes in insurance and credit risk
  • Quantile options and options based on occupation times
  • Stochastic analysis and its applications in financial mathematics
  • Stochastic control and analysis of partial differential equations in mathematical finance

This list is indicative only and by no means exhaustive. For more details about supervisors and their areas of research interests, please see the  Probability in Finance and Insurance research group . You will find links to the web pages of individual members of staff here. If you are interested in applying to undertake PhD research in probability in finance and insurance, you are welcome to contact one of these members of staff regarding a suitable topic for your research proposal. 

Social Statistics

PhD programmes of study in social statistics typically include both methodological development and the application of statistical methods to a social science field or to address new developments in social data, such as in sample surveys or social networks. Research topics may be identified in advance by the applicant or may be arrived at through communication with a potential supervisor. The relative emphasis on methodology/theory vs. application may vary. 

  • Analysis of complex survey data
  • Disclosure risk assessment and statistical disclosure control
  • Estimation from survey data (and related data), taking account of nonresponse and using auxiliary information
  • Latent transition and latent class models for modelling diagnostic tests
  • Latent variable models and structural equation models for categorical data
  • Longitudinal data analysis, especially event history (survival) analysis and dynamic panel models
  • Modelling response strategies and detection of outliers in educational and behavioural sciences
  • Multilevel simultaneous equations modelling of correlated social processes

For more details about potential supervisors and their areas of interest, visit the  Social Statistics research group . If you are interested in applying to undertake PhD research in social statistics, you are welcome to contact one of these members of staff regarding a suitable topic for your research proposal.

Time Series and Statistical Learning

PhD research in time series and statistical learning encompasses many aspects of these disciplines. We are keenly involved in both theoretical developments and practical applications. Current areas of interest include time series (including high-dimensional and non-stationary time series), data science and machine learning, networks (including dynamical networks), high-dimensional inference and dimension reduction, statistical methods for ranking data, spatio-temporal processes, functional data analysis, shape-constrained estimation, multiscale modelling and estimation and change-point detection.

Research topics may be identified in advance by the applicant or may be arrived at through communication with a potential supervisor. The relative emphasis on methodology/theory vs. application may vary.

Suggested PhD research areas include:

  • Automating statistical advice
  • Change detection for complex data
  • Dimension reduction and factor modelling
  • Estimation of stochastic volatility models
  • Financial econometrics
  • Functional data analysis including functional time series
  • High-dimensional time series analysis
  • High-dimensional variable selection
  • Infectious disease modelling
  • Inference for sequential data including change detection in multiple data streams
  • Network time series analysis
  • Nonparametric and semiparametric regression
  • Non-stationary time series analysis
  • Reinforcement learning for time-dependent data
  • Robust statistical analysis for high-dimensional data
  • Shape-constrained methods
  • Spatial econometrics modelling
  • Spatio-temporal modelling
  • Statistical analysis of high-dimensional multi-type recurrent events

For more information, please see the  Time Series and Statistical Learning research group . 

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Top 99+ Trending Statistics Research Topics for Students

statistics research topics

Being a statistics student, finding the best statistics research topics is quite challenging. But not anymore; find the best statistics research topics now!!!

Statistics is one of the tough subjects because it consists of lots of formulas, equations and many more. Therefore the students need to spend their time to understand these concepts. And when it comes to finding the best statistics research project for their topics, statistics students are always looking for someone to help them. 

In this blog, we will share with you the most interesting and trending statistics research topics in 2023. It will not just help you to stand out in your class but also help you to explore more about the world.

If you face any problem regarding statistics, then don’t worry. You can get the best statistics assignment help from one of our experts.

As you know, it is always suggested that you should work on interesting topics. That is why we have mentioned the most interesting research topics for college students and high school students. Here in this blog post, we will share with you the list of 99+ awesome statistics research topics.

Why Do We Need to Have Good Statistics Research Topics?

Table of Contents

Having a good research topic will not just help you score good grades, but it will also allow you to finish your project quickly. Because whenever we work on something interesting, our productivity automatically boosts. Thus, you need not invest lots of time and effort, and you can achieve the best with minimal effort and time. 

What Are Some Interesting Research Topics?

If we talk about the interesting research topics in statistics, it can vary from student to student. But here are the key topics that are quite interesting for almost every student:-

  • Literacy rate in a city.
  • Abortion and pregnancy rate in the USA.
  • Eating disorders in the citizens.
  • Parent role in self-esteem and confidence of the student.
  • Uses of AI in our daily life to business corporates.

Top 99+ Trending Statistics Research Topics For 2023

Here in this section, we will tell you more than 99 trending statistics research topics:

Sports Statistics Research Topics

  • Statistical analysis for legs and head injuries in Football.
  • Statistical analysis for shoulder and knee injuries in MotoGP.
  • Deep statistical evaluation for the doping test in sports from the past decade.
  • Statistical observation on the performance of athletes in the last Olympics.
  • Role and effect of sports in the life of the student.

Psychology Research Topics for Statistics

  • Deep statistical analysis of the effect of obesity on the student’s mental health in high school and college students.
  • Statistical evolution to find out the suicide reason among students and adults.
  • Statistics analysis to find out the effect of divorce on children in a country.
  • Psychology affects women because of the gender gap in specific country areas.
  • Statistics analysis to find out the cause of online bullying in students’ lives. 
  • In Psychology, PTSD and descriptive tendencies are discussed.
  • The function of researchers in statistical testing and probability.
  • Acceptable significance and probability thresholds in clinical Psychology.
  • The utilization of hypothesis and the role of P 0.05 for improved comprehension.
  • What types of statistical data are typically rejected in psychology?
  • The application of basic statistical principles and reasoning in psychological analysis.
  • The role of correlation is when several psychological concepts are at risk.
  • Actual case study learning and modeling are used to generate statistical reports.
  • In psychology, naturalistic observation is used as a research sample.
  • How should descriptive statistics be used to represent behavioral data sets?

Applied Statistics Research Topics

  • Does education have a deep impact on the financial success of an individual?
  • The investment in digital technology is having a meaningful return for corporations?
  • The gap of financial wealth between rich and poor in the USA.
  • A statistical approach to identify the effects of high-frequency trading in financial markets.
  • Statistics analysis to determine the impact of the multi-agent model in financial markets. 

Personalized Medicine Statistics Research Topics

  • Statistical analysis on the effect of methamphetamine on substance abusers.
  • Deep research on the impact of the Corona vaccine on the Omnicrone variant. 
  • Find out the best cancer treatment approach between orthodox therapies and alternative therapies.
  • Statistics analysis to identify the role of genes in the child’s overall immunity.
  • What factors help the patients to survive from Coronavirus .

Experimental Design Statistics Research Topics

  • Generic vs private education is one of the best for the students and has better financial return.
  • Psychology vs physiology: which leads the person not to quit their addictions?
  • Effect of breastmilk vs packed milk on the infant child overall development
  • Which causes more accidents: male alcoholics vs female alcoholics.
  • What causes the student not to reveal the cyberbullying in front of their parents in most cases. 

Easy Statistics Research Topics

  • Application of statistics in the world of data science
  • Statistics for finance: how statistics is helping the company to grow their finance
  • Advantages and disadvantages of Radar chart
  • Minor marriages in south-east Asia and African countries.
  • Discussion of ANOVA and correlation.
  • What statistical methods are most effective for active sports?
  • When measuring the correctness of college tests, a ranking statistical approach is used.
  • Statistics play an important role in Data Mining operations.
  • The practical application of heat estimation in engineering fields.
  • In the field of speech recognition, statistical analysis is used.
  • Estimating probiotics: how much time is necessary for an accurate statistical sample?
  • How will the United States population grow in the next twenty years?
  • The legislation and statistical reports deal with contentious issues.
  • The application of empirical entropy approaches with online grammar checking.
  • Transparency in statistical methodology and the reporting system of the United States Census Bureau.

Statistical Research Topics for High School

  • Uses of statistics in chemometrics
  • Statistics in business analytics and business intelligence
  • Importance of statistics in physics.
  • Deep discussion about multivariate statistics
  • Uses of Statistics in machine learning

Survey Topics for Statistics

  • Gather the data of the most qualified professionals in a specific area.
  • Survey the time wasted by the students in watching Tvs or Netflix.
  • Have a survey the fully vaccinated people in the USA 
  • Gather information on the effect of a government survey on the life of citizens
  • Survey to identify the English speakers in the world.

Statistics Research Paper Topics for Graduates

  • Have a deep decision of Bayes theorems
  • Discuss the Bayesian hierarchical models
  • Analysis of the process of Japanese restaurants. 
  • Deep analysis of Lévy’s continuity theorem
  • Analysis of the principle of maximum entropy

AP Statistics Topics

  • Discuss about the importance of econometrics
  • Analyze the pros and cons of Probit Model
  • Types of probability models and their uses
  • Deep discussion of ortho stochastic matrix
  • Find out the ways to get an adjacency matrix quickly

Good Statistics Research Topics 

  • National income and the regulation of cryptocurrency.
  • The benefits and drawbacks of regression analysis.
  • How can estimate methods be used to correct statistical differences?
  • Mathematical prediction models vs observation tactics.
  • In sociology research, there is bias in quantitative data analysis.
  • Inferential analytical approaches vs. descriptive statistics.
  • How reliable are AI-based methods in statistical analysis?
  • The internet news reporting and the fluctuations: statistics reports.
  • The importance of estimate in modeled statistics and artificial sampling.

Business Statistics Topics

  • Role of statistics in business in 2023
  • Importance of business statistics and analytics
  • What is the role of central tendency and dispersion in statistics
  • Best process of sampling business data.
  • Importance of statistics in big data.
  • The characteristics of business data sampling: benefits and cons of software solutions.
  • How may two different business tasks be tackled concurrently using linear regression analysis?
  • In economic data relations, index numbers, random probability, and correctness are all important.
  • The advantages of a dataset approach to statistics in programming statistics.
  • Commercial statistics: how should the data be prepared for maximum accuracy?

Statistical Research Topics for College Students

  • Evaluate the role of John Tukey’s contribution to statistics.
  • The role of statistics to improve ADHD treatment.
  • The uses and timeline of probability in statistics.
  • Deep analysis of Gertrude Cox’s experimental design in statistics.
  • Discuss about Florence Nightingale in statistics.
  • What sorts of music do college students prefer?
  • The Main Effect of Different Subjects on Student Performance.
  • The Importance of Analytics in Statistics Research.
  • The Influence of a Better Student in Class.
  • Do extracurricular activities help in the transformation of personalities?
  • Backbenchers’ Impact on Class Performance.
  • Medication’s Importance in Class Performance.
  • Are e-books better than traditional books?
  • Choosing aspects of a subject in college

How To Write Good Statistics Research Topics?

So, the main question that arises here is how you can write good statistics research topics. The trick is understanding the methodology that is used to collect and interpret statistical data. However, if you are trying to pick any topic for your statistics project, you must think about it before going any further. 

As a result, it will teach you about the data types that will be researched because the sample will be chosen correctly. On the other hand, your basic outline for choosing the correct topics is as follows:

  • Introduction of a problem
  • Methodology explanation and choice. 
  • Statistical research itself is in the main part (Body Part). 
  • Samples deviations and variables. 
  • Lastly, statistical interpretation is your last part (conclusion). 

Note:   Always include the sources from which you obtained the statistics data.

Top 3 Tips to Choose Good Statistics Research Topics

It can be quite easy for some students to pick a good statistics research topic without the help of an essay writer . But we know that it is not a common scenario for every student. That is why we will mention some of the best tips that will help you choose good statistics research topics for your next project. Either you are in a hurry or have enough time to explore. These tips will help you in every scenario.

1. Narrow down your research topic

We all start with many topics as we are not sure about our specific interests or niche. The initial step to picking up a good research topic for college or school students is to narrow down the research topic.

For this, you need to categorize the matter first. And then pick a specific category as per your interest. After that, brainstorm about the topic’s content and how you can make the points catchy, focused, directional, clear, and specific. 

2. Choose a topic that gives you curiosity

After categorizing the statistics research topics, it is time to pick one from the category. Don’t pick the most common topic because it will not help your grades and knowledge. Instead of it, please choose the best one, in which you have little information, or you are more likely to explore it.

In a statistics research paper, you always can explore something beyond your studies. By doing this, you will be more energetic to work on this project. And you will also feel glad to get them lots of information you were willing to have but didn’t get because of any reasons.

It will also make your professor happy to see your work. Ultimately it will affect your grades with a positive attitude.

3. Choose a manageable topic

Now you have decided on the topic, but you need to make sure that your research topic should be manageable. You will have limited time and resources to complete your project if you pick one of the deep statistics research topics with massive information.

Then you will struggle at the last moment and most probably not going to finish your project on time. Therefore, spend enough time exploring the topic and have a good idea about the time duration and resources you will use for the project. 

Statistics research topics are massive in numbers. Because statistics operations can be performed on anything from our psychology to our fitness. Therefore there are lots more statistics research topics to explore. But if you are not finding it challenging, then you can take the help of our statistics experts . They will help you to pick the most interesting and trending statistics research topics for your projects. 

With this help, you can also save your precious time to invest it in something else. You can also come up with a plethora of topics of your choice and we will help you to pick the best one among them. Apart from that, if you are working on a project and you are not sure whether that is the topic that excites you to work on it or not. Then we can also help you to clear all your doubts on the statistics research topic. 

Frequently Asked Questions

Q1. what are some good topics for the statistics project.

Have a look at some good topics for statistics projects:- 1. Research the average height and physics of basketball players. 2. Birth and death rate in a specific city or country. 3. Study on the obesity rate of children and adults in the USA. 4. The growth rate of China in the past few years 5. Major causes of injury in Football

Q2. What are the topics in statistics?

Statistics has lots of topics. It is hard to cover all of them in a short answer. But here are the major ones: conditional probability, variance, random variable, probability distributions, common discrete, and many more. 

Q3. What are the top 10 research topics?

Here are the top 10 research topics that you can try in 2023:

1. Plant Science 2. Mental health 3. Nutritional Immunology 4. Mood disorders 5. Aging brains 6. Infectious disease 7. Music therapy 8. Political misinformation 9. Canine Connection 10. Sustainable agriculture

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Bar, Haim – "Parallel Testing, and Variable Selection -- a Mixture-Model Approach with Applications in Biostatistics" 

Dissertation Advisor: James Booth

Initial Job Placement: Postdoc, Department of Medicine, Weill Medical Center, New York, NY

Cunningham, Caitlin –  "Markov Methods for Identifying ChIP-seq Peaks" 

Initial Job Placement: Assistant Professor, Le Moyne College, Syracuse, NY

Ji, Pengsheng – "Selected Topics in Nonparametric Testing and Variable Selection for High Dimensional Data" 

Dissertation Advisor: Michael Nussbaum 

Initial Job Placement: Assistant Professor, University of Georgia, Athens, GA

Morris, Darcy Steeg – "Methods for Multivariate Longitudinal Count and Duration Models with Applications in Economics" 

Dissertation Advisor: Francesca Molinari 

Initial Job Placement: Research Mathematical Statistician, Center for Statistical Research and Methodology, U.S. Census Bureau, Washington DC

Narayanan, Rajendran – "Shrinkage Estimation for Penalised Regression, Loss Estimation and Topics on Largest Eigenvalue Distributions" 

Initial Job Placement: Visiting Scientist, Indian Statistical Institute, Kolkata, India

Xiao, Luo – "Topics in Bivariate Spline Smoothing" 

Dissertation Advisor: David Ruppert 

Initial Job Placement: Postdoc, Johns Hopkins University, Baltimore, MD

Zeber, David – "Extremal Properties of Markov Chains and the Conditional Extreme Value Model" 

Dissertation Advisor: Sidney Resnick 

Initial Job Placement: Data Analyst, Mozilla, San Francisco, CA

Clement, David – "Estimating equation methods for longitudinal and survival data" 

Dissertation Advisor: Robert Strawderman 

Initial Job Placement: Quantitative Analyst, Smartodds, London UK

Eilertson, Kirsten – "Estimation and inference of random effect models with applications to population genetics and proteomics" 

Dissertation Advisor: Carlos Bustamante 

Initial Job Placement: Biostatistician, The J. David Gladstone Institutes, San Francisco CA

Grabchak, Michael – "Tempered stable distributions: properties and extensions" 

Dissertation Advisor: Gennady Samorodnitsky 

Initial Job Placement: Assistant Professor, UNC Charlotte, Charlotte NC

Li, Yingxing – "Aspects of penalized splines" 

Initial Job Placement: Assistant Professor, The Wang Yanan Institute for Studies in Economics, Xiamen University

Lopez Oliveros, Luis – "Modeling end-user behavior in data networks" 

Dissertation Advisor: Sidney Resnick  

Initial Job Placement: Consultant, Murex North America, New York NY

Ma, Xin – "Statistical Methods for Genome Variant Calling and Population Genetic Inference from Next-Generation Sequencing Data" 

Initial Job Placement: Postdoc, Stanford University, Stanford CA

Kormaksson, Matthias – "Dynamic path analysis and model based clustering of microarray data" 

Dissertation Advisor: James Booth 

Initial Job Placement: Postdoc, Department of Public Health, Weill Cornell Medical College, New York NY

Schifano, Elizabeth – "Topics in penalized estimation" 

Initial Job Placement: Postdoc, Department of Biostatistics, Harvard University, Boston MA

Hanlon, Bret – "High-dimensional data analysis" 

Dissertation Advisor: Anand Vidyashankar 

Shaby, Benjamin – "Tools for hard bayesian computations" 

Initial Job Placement: Postdoc, SAMSI, Durham NC

Zipunnikov, Vadim – "Topics on generalized linear mixed models" 

Initial Job Placement: Postdoc, Department of Biostatistics, Johns Hopkins University, Baltimore MD

Barger, Kathryn Jo-Anne – "Objective bayesian estimation for the number of classes in a population using Jeffreys and reference priors" 

Dissertation Advisor: John Bunge 

Initial Job Placement: Pfizer Incorporated

Chan, Serena Suewei – "Robust and efficient inference for linear mixed models using skew-normal distributions" 

Initial Job Placement: Statistician, Takeda Pharmaceuticles, Deerfield IL

Lin, Haizhi – "Distressed debt prices and recovery rate estimation" 

Dissertation Advisor: Martin Wells  

Initial Job Placement: Associate, Fixed Income Department, Credit Suisse Securities (USA), New York, NY


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