• Topic on the statistical analysis of high-dimensional data. 

  Stein, Caleb Karl, 1985- (2019-04-15)

  High-dimensional genomic data can provide deep insight into biological processes. However, conventional statistical methods typically cannot be applied directly to genomic data sets because the high dimensionality of markers ...

• A power contrast of tests for homogeneity of covariance matrices in a high-dimensional setting. 

  Barnard, Ben Joseph, 1987-; 0000-0002-1817-2295 (2018-10-31)

  Multivariate statistical analyses, such as linear discriminant analysis, MANOVA, and profile analysis, have a covariance-matrix homogeneity assumption. Until recently, homogeneity testing of covariance matrices was limited ...

• Bayesian approach to partially validated binary regression with response and exposure misclassification. 

  Anderson, Katrina Julie, 1987-; 0000-0002-3189-871X (2018-06-09)

  Misclassification of epidemiological and observational data is a problem that commonly arises and can have adverse ramifications on the validity of results if not properly handled. Considerable research has been conducted ...

• Weibull mixture model for grouped data and pattern identification in spatial and spatial-temporal data. 

  Yu, Youjiao, 1987-; 0000-0003-0519-9605 (2018-06-22)

  Motived by problems in geology, civil engineering, and material science, we develop statistical models and apply statistical tools for characterizing patterns that exist in different types of data. In the first project, ...

• On inferring cognitive impairment from a battery of tests and predicting an event-of-interest using longitudinal and time-to-event data. 

  McCreary, Morgan C., 1988- (2018-06-29)

  In this dissertation we first investigated two commonly used methods’ and a recently proposed method’s ability to predict conditional survival probabilities based on longitudinal biomarker measurements. Then, in preparation ...

• Bayesian propensity score analysis for clustered observational studies. 

  Zhou, Qi, 1990- (2018-04-12)

  There is increasing demand to investigate questions in observational study. The propensity score is a popular confounding adjustment technique to ensure valid causal inference for observational study. Observational data ...

• Beta regression for modeling a covariate-adjusted ROC. 

  Stanley, Sarah Elizabeth, 1992-; 0000-0002-0740-8688 (2018-03-12)

  The receiver operating characteristic (ROC) curve is a well-accepted measure of accuracy for diagnostic tests. In many applications, test performance is affected by covariates. As a result, several regression methodologies ...

• Applications of Bayesian quantile regression and sample size determination. 

  King, J. Clay, 1984- (2018-03-21)

  Bayesian statistical methods reverse the philosophy of traditional statistical practice by treating parameters as random, rather than fixed. In so doing, Bayesian methods are able to incorporate uncertainty about parameter ...

• Three applications of linear dimension reduction. 

  Odom, Gabriel Jairus. 1988- (2017-08-23)

  Linear Dimension Reduction (LDR) has many uses in engineering, business, medicine, economics, data science and others. LDR can be employed when observations are recorded with many correlated features to reduce the number ...

• Bayesian inference for bivariate Poisson data with zero-inflation. 

  Drevets, Madeline L., 1991- (2017-07-27)

  Multivariate count data with zero-inflation is common throughout pure and applied science. Such count data often includes excess zeros. Zero-inflated Poisson regression models have been used in several applications to model ...

• Graphical methods in prior elicitation. 

  Casement, Christopher James, 1987- (2017-07-16)

  Prior elicitation is the process of quantifying an expert's belief in the form of a probability distribution on a parameter(s) to be used in a Bayesian data analysis. Existing methods require experts to quantify their ...

• Binomial and Poisson regression with misclassified outcomes and binary covariates : a Bayesian approach. 

  Nelson, Tyler Wells, 1990- (2017-07-20)

  Misclassification of an outcome and/or covariate is present in many regression applications due to the inability to have a "gold standard''. Therefore, fallible measurement methods are used when infallible tests are ...

• Bayesian models for unmeasured confounder in the analysis of time-to-event data. 

  Chen, Wencong, 1981- (2016-03-23)

  Observational studies that omit confounders are subject to bias. In this dissertation we consider the specific case of time-to-event data. We also provide both the Bayesian parametric and the semi-parametric “twin regression” ...

• Bayesian methods to estimate the accuracy of a binary measurement system. 

  Eschmann, Mark, 1984- (2016-04-19)

  Binary Measurement Systems (BMS) are frequently used in such applications as quality control. They are important enough that their operating characteristics, the repeatability and reproducibility are important because of ...

• Logistic regression models for short sequences of correlated binary variables possessing first-order Markov dependence. 

  Marcovitz, Michelle S.; 0000-0002-0871-0897 (2015-07-23)

  In this dissertation we consider a first-order Markov dependence model for a short sequence of correlated Bernoulli random variables. Specifically, we offer logistic regression models with first-order Markov dependency, ...

• Topics in Bayesian models with ordered parameters : response misclassification, covariate misclassification, and sample size determination. 

  Tecson, Kristen M., 1989- (2015-06-30)

  Researchers often analyze data assuming models with constrained parameters. Order constrained parameters are of particular interest. In this dissertation, we examine three Bayesian models which incorporate ordered parameters. ...

• Normal approximation for Bayesian models with non-sampling bias. 

  Yuan, Jiang, 1984- (, 2014-01-28)

  Bayesian sample size determination can be computationally intensive for mod- els where Markov chain Monte Carlo (MCMC) methods are commonly used for in- ference. It is also common in a large database where the unmeasured ...

• Sample size determination for two sample binomial and Poisson data models based on Bayesian decision theory. 

  Sides, Ryan A. (, 2014-01-28)

  Sample size determination continues to be an important research area in statistical analysis due to the cost and time constraints that often exist in areas such as pharmaceuticals and public health. We begin by outlining ...

• Topics in interval estimation for two problems using double sampling. 

  Njoh, Linda. (, 2014-01-28)

  This dissertation addresses two distinct topics. The first considers interval estimation methods of the odds ratio parameter in two by two cohort studies with misclassified data. That is, we derive two first-order ...

• Topics in multivariate covariance estimation and time series analysis. 

  Beeson, John D. (John David) (, 2014-01-28)

  In this dissertation we will discuss two topics relevant to statistical analysis. The first is a new test of linearity for a stationary time series, that extends the bootstrap methods of Berg et al. (2010) to goodness-of-fit ...

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