Department of Statistical Sciences
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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 ... 
Topics in odds ratio estimation in the casecontrol studies and the bioequivalence testing in the crossover studies.
(, 20111219)The doublesampling paradigm, which has become an important part of the epidemiological designs, includes two stages. First, individuals are classified into groups by disease and exposure levels using a fallible test, and ... 
Bayesian topics in biostatistics : treatment selection, sample size, power, and misclassification.
(, 20111219)Bayesian methodology is implemented to investigate three problems in biostatistics. The first problem considers using biomarkers to select optimal treatments for individual patients. A Bayesian adaptation of the selection ... 
Poisson regression models for interval censored count data.
(20110512)In this dissertation, we develop Bayesian models for interval censored Poisson counts in the presence of zero inflation and missing data. As a motivating example, we consider data arising from a Human Immunodeﬁciency Virus ... 
Interval estimation for TPRs and FPRs of two diagnostic tests with unverified negatives.
(20110512)In the clinical setting, the performance of a diagnostic or screening test is often summarized using the test's true positive rate (TPR) and false positive rate (FPR). However, estimation of the TPR and FPR for a diagnostic ... 
Bayesian samplesize determination and adaptive design for clinical trials with Poisson outcomes.
(Elsevier., 2010)Because of the high cost and time constraints for clinical trials, researchers often need to determine the smallest sample size that provides accurate inferences for a parameter of interest or need to adaptive design ... 
Bayesian approaches to correcting bias in epidemiological data.
(20110512)Bias in parameter estimation of count data is a common concern. The concern is even greater when all counts are not recorded. Failing to adjust for underreported data can lead to incorrect parameter estimates. A Bayesian ... 
Bayesian and likelihoodbased interval estimation for the risk ratio using double sampling with misclassified binomial data.
(20110105)We consider the problem of point and interval estimation for the risk ratio using double sampling with twosample misclassified binary data. For such data, it is wellknown that the actual data model is unidentifiable. To ... 
Spatial Poisson regression : Bayesian approach correcting for measurement error with applications.
(20101008)Under and over reporting is a common problem in social science research, adverse events associated with drug use, and many other areas of research. Furthermore, overdispersion is another common problem that plagues count ...