• Graph two-sample test via empirical likelihood 

  Zou, Ting (North Dakota State University, 2024)

  In the past two decades, there has been a notable surge in network data. This proliferation has spurred significant advancements in methods for analyzing networks across various disciplines, including computer science, ...

• A Comparison of the Ansari-Bradley Test and the Moses Test for the Variances 

  Yuni, Chen (North Dakota State University, 2011)

  This paper is aimed to compare the powers and significance levels of two well known nonparametric tests: the Ansari-Bradley test and the Moses test in both situations where the equal-median assumption is satisfied and ...

• Estimating the Number of Genes That Are Differentially Expressed in Two Dependent Experiments or Analyses 

  Lawal, Hammed (North Dakota State University, 2022)

  Many researchers have used the intersection method to compare the results of differential expression analysis between two or more gene expression experiments. Some methods have been proposed to estimate the number of genes ...

• An Analysis of the NBA Draft: Are Teams Drafting Better and Does College Experience Truly Matter 

  Wolfe, Kevin (North Dakota State University, 2022)

  This thesis attempts to answer two questions. Are NBA organizations doing a reasonable job at drafting players and getting better at the process, and does college experience play a significant role in a player’s performance ...

• A Comparative Multiple Simulation Study for Parametric and Nonparametric Methods in the Identification of Differentially Expressed Genes 

  Palmer, Daniel Grant (North Dakota State University, 2021)

  RNA-seq data simulated from a negative binomial distribution, sampled without replacement, or modified from read counts were analyzed to compare differential gene expression analysis methods in terms of false discovery ...

• Robustness of the Eigenvalue Test for Community Structure 

  Rose, Matthew Allan (North Dakota State University, 2021)

  Networks can take on many different forms, such as the people from the University you attend. Within these networks, community structure may exist. This "community structure" refers to the clustering of nodes by a common ...

• A Spectral Two Sample Test 

  Xu, Yangshuang (North Dakota State University, 2021)

  In statistics, two-sample tests are commonly desired under many topics. It's crucial to differentiate populations as a prerequisite for developing further analysis. A number of statistical tests have been developed for ...

• Survival Analysis of Treatment Effect For Brain Cancer Based on The Surveillance, Epidemiology, and End Results Database 

  Mathiason, Madison Jane (North Dakota State University, 2020)

  Cancer is one of the leading causes of death in the United States. The Surveillance, Epidemiology, and End Results (SEER) data from the National Cancer Institute is a population based cancer registry, which geographically ...

• Propensity Score and Survival Analysis for Lung Cancer 

  Mostofa, Mohammad Gulam (North Dakota State University, 2020)

  Propensity scores were used to assess covariate balance between black and white groups in each lung cancer stage of a large data set. Pairwise log rank tests were used to test the equality of survival distribution for ...

• The Determinants of Aeronautical Charges of U.S.Airports: A Spatial Analysis 

  Karanki, Fecri (North Dakota State University, 2020)

  Using U.S. airport data from 2009 through 2016, this thesis examines the determinants of aeronautical charges of large and medium hub airports and accounts for the spatial dependence of neighboring airports in a spatial ...

• A Comparison of Filtering and Normalization Methods in the Statistical Analysis of Gene Expression Experiments 

  Speicher, Mackenzie Rosa Marie (North Dakota State University, 2020)

  Both microarray and RNA-seq technologies are powerful tools which are commonly used in differential expression (DE) analysis. Gene expression levels are compared across treatment groups to determine which genes are ...

• Comparing Performance of ANOVA to Poisson and Negative Binomial Regression When Applied to Count Data 

  Soumare, Ibrahim (North Dakota State University, 2020)

  Analysis of Variance (ANOVA) is the easiest and most widely used model nowadays in statistics. ANOVA however requires a set of assumptions for the model to be a valid choice and for the inferences to be accurate. Among ...

• Investigating Gender Bias Among Grant Applicants 

  Heim, Michael Thomas (North Dakota State University, 2020)

  An ongoing debate in society is about the existence of a wage gap between genders, and society’s alleged preference to hire a man over an equally qualified woman. This debate extends from the commercial employment world ...

• Empirical Comparison of Statistical Tests of Dense Subgraph in Network Data 

  Fornshell, Caleb Joseph (North Dakota State University, 2020)

  Network analysis is useful in modeling the structures of different phenomena. A fundamental question in the analysis of network data is whether a network contains community structure. One type of community structure of ...

• Factors Associated with Teacher Preparedness and Career Satisfaction in First Year Teachers 

  Buth, Kevin Ross (North Dakota State University, 2020)

  The objective of this study is to determine the potential association between teaching state, subject taught, perceived preparation given by teacher preparedness programs, and perceived support from administration and ...

• Linear Modeling of Election Results for U.S. House of Representatives Candidates and State Executive Offices for Iowa, Minnesota, and North Dakota 

  McEwen, Christopher (North Dakota State University, 2020)

  Better understanding the relationship between the results for the U.S. House of Representatives and for state executive offices could potentially be useful in predicting outcomes if a significant relationship is present ...

• A Comparison of Two Scaling Techniques to Reduce Uncertainty in Predictive Models 

  Todd, Austin Luke (North Dakota State University, 2020)

  This research examines the use of two scaling techniques to accurately transfer information from small-scale data to large-scale predictions in a handful of nonlinear functions. The two techniques are (1) using random draws ...

• Type I Error Assessment and Power Comparison of ANOVA and Zero-Inflated Methods on Zero-Inflated Data 

  Young, Lucas Blackmore (North Dakota State University, 2019)

  Many tests for the analysis of continuous data have the underlying assumption that the data in question follows a normal distribution (ex. ANOVA, regression, etc.). Within certain research topics, it is common to end up ...

• Bayesian Sparse Factor Analysis of High Dimensional Gene Expression Data 

  Zhao, Jingjun (North Dakota State University, 2019)

  This work closely studied fundamental techniques of Bayesian sparse Factor Analysis model - constrained Least Square regression, Bayesian Lasso regression, and some popular sparsity-inducing priors. In Appendix A, we ...

• Empirical Study of Two Hypothesis Test Methods for Community Structure in Networks 

  Nan, Yehong (North Dakota State University, 2019)

  Many real-world network data can be formulated as graphs, where a binary relation exists between nodes. One of the fundamental problems in network data analysis is community detection, clustering the nodes into different ...

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