statistics master's thesis topics

Master's Thesis

As part of the Completion Exercise for the Master's of Science in Statistical Science degree, you may write and present your Master's Thesis . This oral examination is administered by your Master's Committee. Students choosing to defend a thesis should begin work on their research as early as possible, preferably in their second semester or summer after their first year in the program. Please give yourself enough time to write your thesis. Your thesis advisor (chair of your committee) should approve your thesis title. The work has to be approved by all members of your committee.

Master’s BEST Award: Each 2 nd year Duke Master’s of Statistical Science (MSS) student defending their MSS thesis may be eligible for the Master’s BEST Award . The selection of the awardee is made by the Statistical Science faculty BEST Award Committee on the basis of the submitted thesis of MSS thesis students in the same year.

All students choosing to do a thesis should submit a thesis proposal (not more than two pages) to the MS Director via Qualtrics by October 15th (of your third semester). The thesis proposal should include a title (it can be tentative and refined later), a list of three committee members (two should be from the Statistics Department, including the chair), and a description of your work.

Please note: The Master's Thesis Committee should be formed and approved by The Graduate School at least 30 days prior to your thesis defense.

For details, see the document below.

The Thesis consists of a detailed written report on a project approved by the M.S. Director and the student's thesis advisor, covering aspects of your contribution to the project area :

introduction
summary of contributions and results
discussion of open questions
bibliographic material

The Master's Thesis and its submission must conform to the Duke University Graduate School M.S. thesis requirements . All students choosing to do Master's Thesis should follow the steps outlined in the MSS Thesis Defense Process document.

Hierarchical Signal Propagation for Household Level Sales in Bayesian Dynamic Models
Logistic Tree Gaussian Processes (LoTgGaP) for Microbiome Dynamics and Treatment Effects
Bayesian Inference on Ratios Subject to Differentially Private Noise
Multiple Imputation Inferences for Count Data
An Euler Characteristic Curve Based Representation of 3D Shapes in Statistical Analysis
An Investigation Into the Bias & Variance of Almost Matching Exactly Methods
Comparison of Bayesian Inference Methods for Probit Network Models
Differentially Private Counts with Additive Constraints
Multi-Scale Graph Principal Component Analysis for Connectomics
MCMC Sampling Geospatial Partitions for Linear Models
Bayesian Dynamic Network Modeling with Censored Flow Data
An Application of Graph Diffusion for Gesture Classification
Easy and Efficient Bayesian Infinite Factor Analysis
Analyzing Amazon CD Reviews with Bayesian Monitoring and Machine Learning Methods
Missing Data Imputation for Voter Turnout Using Auxiliary Margins
Generalized and Scalable Optimal Sparse Decision Trees
Construction of Objective Bayesian Prior from Bertrand’s Paradox and the Principle of Indifference
Rethinking Non-Linear Instrumental Variables
Clustering-Enhanced Stochastic Gradient MCMC for Hidden Markov Models
Optimal Sparse Decision Trees
Bayesian Density Regression with a Jump Discontinuity at a Given Threshold
Forecasting the Term Structure of Interest Rates: A Bayesian Dynamic Graphical Modeling Approach
Testing Between Different Types of Poisson Mixtures with Applications to Neuroscience
Multiple Imputation of Missing Covariates in Randomized Controlled Trials
A Bayesian Strategy to the 20 Question Game with Applications to Recommender Systems
Applied Factor Dynamic Analysis for Macroeconomic Forecasting
A Theory of Statistical Inference for Ensuring the Robustness of Scientific Results
Bayesian Inference Via Partitioning Under Differential Privacy
A Bayesian Forward Simulation Approach to Establishing a Realistic Prior Model for Complex Geometrical Objects
Two Applications of Summary Statistics: Integrating Information Across Genes and Confidence Intervals with Missing Data
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Statistics and Actuarial Science

Graduate theses.

Below is a list of the theses produced by graduate students in the Department of Statistics and Actuarial Science.

Projects and Theses From Previous Years

2015 - 2019 2010 - 2014 2005 - 2009 2000 - 2004 1990's 1980's and prior

The topic suggestions are grouped by the specialisations where they best fit in. The list is not exhaustive and you don't have to choose a topic from the list, you can agree something different with your supervisor. You can find completed theses on the University of Helsinki E-thesis service .

If you are interested in doing your Master's thesis on one of the suggested topics, please contact the supervisor responsible for the topic. The lists are continuously updated and topics takes are replaced with new suggestions.

You can also find some thesis topics listed in Overleaf.

A badly focused photograph can be sharpened digitally. In mathematical terms, this is an inverse problem called deconvolution. The topic of the master’s thesis is to apply analytical and learning methods to blurred photographs and compare their performance. Analytical deconvolution methods include Tikhonov regularisation with preconditioning and total variation regularisation. Machine learning should be done using convolutional neural networks (CNNs). Prerequisites: course "Inverse problems 1: convolution and deconvolution” (MAST31401) and some experience in machine learning programming.

Sometimes image classifiers based on convolutional neural networks (CNNs) can be fooled by structured noise. The thesis topic is to examine whether such a vulnerability can be overcome by total variation regularisation as a preprocessing step. Prerequisites: course "Inverse problems 1: convolution and deconvolution” (MAST31401) and some experience in machine learning programming.

Image noise removal, or denoising, is one of the standard challenges in image processing. The mathematics of inverse problems offers suitable methods, for example Total Variation (TV) regularisation. However, TV regularisation involves a parameter controlling the strength of the denoising. Watch this video .

Optimal choice of the parameter is a deep question in inverse problems research. There are several methods in the literature. Int this MSc thesis project you get to implement a web-based test for human subjects. That way we can find what parameter value people prefer. That is then compared to one of the automatic methods. Prerequisites for the project are courses Applications of matrix computations and Inverse Problems I: convolution and deconvolution. Also, some programming experience is needed.

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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-research-topics

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.

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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.

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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.

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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..

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List of Best Statistics Research Topics with Objectives

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.

View different varieties of dissertation topics and samples on multiple subjects for every educational level

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M.S. Program

Advising The vice chair for graduate studies is the chief graduate adviser and heads a committee of faculty advisers who may serve as academic advisers. The research interests of the members of this committee span most of the major areas of statistics. During their first quarter in the program students are required to meet with an academic adviser who assists them in planning a reasonable course of study. In addition, the academic adviser is responsible for monitoring the student’s degree progress and approving the study list each quarter. After the student identifies a thesis topic, the chair of the thesis committee becomes the student’s academic adviser.

Continuing students should meet with either the vice chair for graduate studies or their academic adviser at least once each quarter and a record of this interview is placed in the student’s academic file. Each fall a committee consisting of all regular departmental faculty meet to evaluate the progress of all enrolled M.S. degree students. This committee decides if students are making satisfactory progress, and if not offers specific recommendations to correct the situation. For students who have begun thesis work, the determination of satisfactory progress is typically delegated to the academic adviser. Students who are found to be consistently performing unsatisfactorily may be recommended for termination by a vote of this committee.

Areas of Study The strengths of current and prospective faculty dictate the specific fields of emphasis in the department: applied multivariate analysis; bioinformatics ( Center for Statistical Research in Computational Biology ); computational and computer-intensive statistics; computer vision; cognition; artificial intelligence; machine learning ( Center for Vision, Cognition, Learning, and Autonomy ); social statistics ( Center for Social Statistics ); experimental design and environmental statistics.

Foreign Language Requirement None.

Course Requirements 44 units of course work are required for the M.S. degree, of which at least 32 units must be graduate courses (200 series), while the remaining 12 units may be approved upper division (100 series) courses. With consent of either the vice chair for graduate studies or their academic adviser, students may take up to 20 units of the required 44 units in other departments provided that these courses are in professional or scientific fields closely related to research in statistics. All courses must be passed with the grade of B- or better and students must maintain an overall grade-point average of 3.0 or better. Students may enroll in Statistics 596 any number of times and may apply up to eight units of 596 courses toward the 44-unit requirement and the 32-unit graduate course requirement for the M.S. degree, provided a B- or better (not the grade of S) is received in these courses. Students are required to enroll in Statistics 290 for at least three quarters, and are strongly encouraged to take Statistics 200A-200B-200C, 201A-201B-201C, and 202A-202B-202C in their first year.

Students with gaps in their previous training are allowed to take, with the approval of their academic adviser, undergraduate courses offered by the department. However, Statistics 100A-100B-100C, 101A-101B-101C may not be applied toward course requirements for a graduate degree in the department. Students who need a basic refresher course are encouraged to take Statistic 100A-100B.

Teaching Experience Not required. Students who wish to serve as teaching assistants in the department must have taken or be currently enrolled in Statistics 495A-495B-495C.

Field Experience Not required.

Capstone Plan This plan is not available to master’s degree students.

Thesis Plan Every master’s degree thesis plan requires the completion of an approved thesis that demonstrates the student’s ability to perform original, independent research.

This plan is for master’s degree students only. Students must find a thesis adviser, who approves the topic and form of the thesis. Students must nominate a thesis committee consisting of the adviser and at least two other faculty members who are eligible to serve on thesis committees, and the committee must be appointed by the Graduate Division. The final thesis must be approved by the thesis committee.

Time-to-Degree Students are expected to complete the requirements for the M.S. degree in six quarters of full-time study. In order for a student to complete their degree, they must submit an electronic version of the final thesis to [email protected] .

For Students Who Entered Before Fall 2022 Please click this link . Then navigate to “Program Requirements” in the tab that opens and select the academic year when you matriculated.

Timeline to Filing Your Thesis

Please bookmark the following links for the school’s more detailed calendar and deadlines: https://www.registrar.ucla.edu/Term-Calendar https://grad.ucla.edu/academics/calendar/

Termination of Graduate Study and Appeal of Termination — University Policy A student who fails to meet the above requirements may be recommended for termination of graduate study. A graduate student may be disqualified from continuing in the graduate program for a variety of reasons. The most common is failure to maintain the minimum cumulative grade point average (3.00) required by the Academic Senate to remain in good standing (some programs require a higher grade point average). Other examples include failure of examinations, lack of timely progress toward the degree and poor performance in core courses. Probationary students (those with cumulative grade point averages below 3.00) are subject to immediate dismissal upon the recommendation of their department. University guidelines governing termination of graduate students, including the appeal procedure, are outlined in Standards and Procedures for Graduate Study at UCLA.

Termination of Graduate Study and Appeal of Termination — Special Departmental / Program Policy for the M.S. Program A student who does not complete all the requirements for the M.S. degree within nine quarters of full-time study is subject to a recommendation for termination. The graduate vice chair decides in each case whether a recommendation for termination is warranted. A student may appeal a recommendation for termination to the Graduate Studies Committee, which makes the final departmental decision.

Faculty Research Interest See the faculty directory listing for current members and their interests at http://directory.stat.ucla.edu/ .

Articulated Masters Program Applications are accepted once a year. All students who are interested in articulated M.S. degrees must apply online by the deadline Feb. 1 as all other M.S. applicants do (same requirements and the same procedure). You must apply as a “new” student and not “continuing”. The admission committee will then make recommendations in the M.S. admission process.

Please note that the Articulated Degree Program should not be confused with a Concurrent Degree Program. The UCLA Graduate Division explains the difference between the two at this link . For more information please carefully read the section entitled Individually Designed Articulated Degree Program at this link .

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COMMENTS

Master's Thesis
Master's Thesis. As part of the Completion Exercise for the Master's of Science in Statistical Science degree, you may write and present your Master's Thesis. This oral examination is administered by your Master's Committee. Students choosing to defend a thesis should begin work on their research as early as possible, preferably in their second semester or summer after their first year in the program.
Graduate Theses
Graduate Theses - Statistics and Actuarial Science - Simon Fraser University Statistics and Actuarial Science Research Resources Graduate Theses Graduate Theses Below is a list of the theses produced by graduate students in the Department of Statistics and Actuarial Science. Projects and Theses From Previous Years
Master's thesis topics
The topic of the master’s thesis is to apply analytical and learning methods to blurred photographs and compare their performance. Analytical deconvolution methods include Tikhonov regularisation with preconditioning and total variation regularisation. Machine learning should be done using convolutional neural networks (CNNs).
List of Best Statistics Research Topics & Ideas (2021-22)
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.
M.S. Program
The strengths of current and prospective faculty dictate the specific fields of emphasis in the department: applied multivariate analysis; bioinformatics (Center for Statistical Research in Computational Biology); computational and computer-intensive statistics; computer vision; cognition; artificial intelligence; machine learning (Center for Vision, Cognition, Learning, and Autonomy); social statistics (Center for Social Statistics); experimental design and environmental statistics.