Consider two models M1 and M2 proposed as models for the same data. Assume that the
conclusions from both models are posteriors and of some inferential target θ given the data …

In this dissertation we develop an innovative approach to analyze the scientific studies using
the generalized linear models (GLM) and beyond. We develop the regression estimator, a …

This paper develops a Bayesian approach to obtain the joint estimates of unknown
parameters, nonparametric functions and random effects in generalized partially linear …

We consider methods for critical assessment of the appropriateness of fitted Bayesian
models. This is done for situations where no particular alternative model is specified, and …

The purpose of this paper is to develop a Bayesian analysis for the zero-inflated hyper-
Poisson model. Markov chain Monte Carlo methods are used to develop a Bayesian …

Although Bayesian inference is a popular paradigm among a large segment of scientists,
including statisticians, most applications consider objective priors and need critical …

In this work, we proposed the semi-parametric cure rate models with independent and
dependent spatial frailties. These models extend the proportional odds cure models and …

ABSTRACT WANG, LIWEI. Nonparametric Models for Longitudinal Data Using Bernstein
Polynomial Sieve.(Under the direction of Sujit K. Ghosh.) Analysis of longitudinal data within …

Influence functions are considered as diagnostics for model departures in parametric
Bayesian inference. A baseline model density is expressed as a mixture; then the mixing …

In discrete choice experiments, patients are presented with sets of health states described
by various attributes and asked to make choices from among them. Discrete choice …