Theses/Dissertations  Statistical Sciences
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Bayesian propensity score analysis for clustered observational studies.
(20180412)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 covariateadjusted ROC.
(20180312)The receiver operating characteristic (ROC) curve is a wellaccepted 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.
(20180321)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 ... 
Bayesian inference for bivariate Poisson data with zeroinflation.
(20170727)Multivariate count data with zeroinflation is common throughout pure and applied science. Such count data often includes excess zeros. Zeroinflated Poisson regression models have been used in several applications to model ... 
Graphical methods in prior elicitation.
(20170716)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.
(20170720)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 timetoevent data.
(20160323)Observational studies that omit confounders are subject to bias. In this dissertation we consider the specific case of timetoevent data. We also provide both the Bayesian parametric and the semiparametric “twin regression” ... 
Bayesian methods to estimate the accuracy of a binary measurement system.
(20160419)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 firstorder Markov dependence.
(20150723)In this dissertation we consider a firstorder Markov dependence model for a short sequence of correlated Bernoulli random variables. Specifically, we offer logistic regression models with firstorder Markov dependency, ... 
Topics in Bayesian models with ordered parameters : response misclassification, covariate misclassification, and sample size determination.
(20150630)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 nonsampling bias.
(, 20140128)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.
(, 20140128)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.
(, 20140128)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 firstorder ... 
Topics in multivariate covariance estimation and time series analysis.
(, 20140128)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 goodnessoffit ... 
Semiparametric estimation and forecasting for functionalcoefficient autoregressive models.
(, 20130916)The functionalcoefficient autoregressive (FCAR) model is a useful structure for reducing the size of the class of nonlinear time series models. Local linear regression has been shown to be an effective method for estimating ... 
A bivariate regression model with correlated mixed responses.
(, 20130916)In the dissertation we consider a bivariate model for associated binary and continuous responses such as those in a clinical trial where both safety and efficacy are observed. We designate a marginal and conditional model ... 
Bayesian approaches for design of psychometric studies with underreporting and misclassification.
(, 20130515)Measurement error problems in binary regression are of considerable interest among researchers, especially in epidemiological studies. Misclassification can be considered a special case of measurement error specifically ... 
Bayesian modelling of mixed outcome types using random effect.
(20121129)The problem of analyzing associated outcomes of mixed type arises frequently in practice. In this dissertation we develop several Bayesian models for analyzing associated discrete and continuous responses simultaneously ... 
Intervalcensored negative binomial models : a Bayesian approach.
(, 20121129)Count data are quite common in many research areas. Intervalcensored counts, in which an interval representing a range of counts is observed rather than the precise count, may arise in many situations, including survey ... 
Selected topics in highdimensional statistical learning.
(, 20121129)Advances in microarray technology have equipped researchers to measure gene expression levels simultaneously from thousands of genes, yielding increasingly large and complex data sets. However, due to the cost and time ...