Department of Statistical Sciences
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Bayesian approaches for survival data in pharmaceutical research.
(2020-09-15)In this research, we consider Bayesian methodologies to address problems in biopharmaceutical research, most of which are motivated by real-world problems in network meta-analysis, prior elicitation, and adaptive designs. ... -
A beta regression approach to nonparametric longitudinal data classification in clinical trials.
(2022-04-07)Classification is an important topic in statistical analysis. For example, in applications involving clinical trials, an often seen objective is to determine whether or not novel medicines and treatments differ from existing ... -
Covariate-adjusted ROC regressions and the extensions in trend tests.
(2022-04-21)In 2008 the faculty and doctoral graduate students within the Department of Statistical Sciences at Baylor University met with statisticians from Eli Lilly and Company to discuss ongoing long-term problems with the possibility ... -
Contributions to the practical application of Bayesian methods to survival analysis in clinical trials.
(2022-03-11)This dissertation is composed of three chapters that deal with fairly distinct concepts. In the first chapter, we compare and contrast the major Bayesian computational platforms accessible in the R statistical computing ... -
Statistical methods for complex spatial data.
(2021-07-12)Spatial analysis is an active research area as it allows us to solve problems containing geographic information in various applications. In this dissertation, we consider some challenging issues we often face in practice. ... -
Contributions to algebraic pattern recognition and integrated likelihood ratio confidence intervals.
(2021-04-12)Nonlinear polynomial equations describing positive-dimensional solution sets are called real varieties that in general may be quite complex but locally look like smooth manifolds. In the first chapter, we describe ways to ... -
Bayesian spatial misclassification model for areal count data with applications to COVID-19.
(2021-04-21)As of December 14, 2020, there have been more than 72.1 million confirmed cases, of which more than 1.61 million have died of COVID-19 globally. In the United States, there are more than 16,200,000 confirmed cases and ... -
Integrated-likelihood-ratio confidence intervals obtained from data via a double-sampling scenario.
(2020-08-26)Hypothesis testing has been a primary focus of statistical inference. Recently, confidence intervals (CIs) have been suggested as a superior inference form because of the additional information they provide to a scientist ... -
Multivariate fault detection and isolation.
(2020-06-08)In a variety of industrial settings, many complexly related variables are monitored to ensure that a process remains in control (IC) over time. Faults that remain undetected can cause extensive damage that require costly ... -
Lehmann ROC regression and spatial classification.
(2019-12-17)Receiver Operating Curves (ROC) are a widely used measure of accuracy in diagnostic tests. Recently, there has been an increased interest in the effect that covariates have on the accuracy of the tests. As a result, several ... -
Detecting episodes of star formation using Bayesian model selection.
(2019-10-30)Bayesian model comparison is a data-driven method to establish model complexity. In this dissertation we investigate its use in detecting multiple episodes of star formation from the analysis of the Spectral Energy ... -
On testing for a difference in two high-dimensional mean vectors.
(2019-11-18)A common problem in multivariate statistical analysis involves testing for differences in the mean vectors from two populations with equal covariance matrices.This problem is considered well-posed when the sum of the two ... -
Bayesian adjustment for misclassification bias and prior elicitation for dependent parameters.
(2019-11-25)This research is motivated by problems in biopharmaceutical research. Prior elicitation is defined as formulating an expert's beliefs about one or more uncertain quantities into a joint probability distribution, and is ... -
Bayesian inference for vaccine efficacy and prediction of survival probability in prime-boost vaccination regimes.
(2019-11-08)This dissertation consists of two major topics on applying Bayesian statistical methods in vaccine development. Chapter two concerns the estimation of vaccine efficacy from validation samples with selection bias. Since ... -
Computational Bayesian methods applied to complex problems in bio and astro statistics.
(2019-09-05)In this dissertation we apply computational Bayesian methods to three distinct problems. In the first chapter, we address the issue of unrealistic covariance matrices used to estimate collision probabilities. We model ... -
Contributions to computational algebraic statistics.
(2019-07-17)The field of algebraic statistics arose from the realization that problems in statistics can often be viewed, and subsequently solved, using algebraic concepts and tools. In this work, we explore the application of algebraic ... -
Topic on the statistical analysis of high-dimensional data.
(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 ... -
Adaptive designs for phase II clinical trials with binary endpoints.
(, 2019-02-01)Because the sample size is varying while the estimate of sample size is changing, the quality of an approximation of the binomial by the Gaussian is variable and thus not desirable. Also, adaptive designs do not follow the ... -
A power contrast of tests for homogeneity of covariance matrices in a high-dimensional setting.
(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.
(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 ...
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