Applied statisticians in many fields frequently analyze time-to-event data. While the statistical
tools presented in this book are applicable to data from medicine, biology, public health …

Survival analysis arises in many fields of study including medicine, biology, engineering,
public health, epidemiology, and economics. This book provides a comprehensive treatment …

The use of the Gibbs sampler for Bayesian computation is reviewed and illustrated in the
context of some canonical examples. Other Markov chain Monte Carlo simulation methods …

Current Gibbs sampling schemes in mixture of Dirichlet process (MDP) models are restricted
to using “conjugate” base measures that allow analytic evaluation of the transition …

The problem of estimating many normal means is approached by means of an hierarchical
model. The hierarchical model is the standard conjugate model with one exception: the …

In his 1972 review of Bayesian statistics, Dennis Lindley identi ed as a success story for
Bayesian ideas the advances made in problems of many parameters and the growth of what …

Appropriate models in biostatistics are often quite complicated. Such models are typically
most easily fit using Bayesian methods, which can often be implemented using simulation …

This volume describes how to conceptualize, perform, and critique traditional generalized
linear models (GLMs) from a Bayesian perspective and how to use modern computational …

There are two generations of Gibbs sampling methods for semiparametric models involving
the Dirichlet process. The first generation suffered from a severe drawback: the locations of …

We develop a dependent Dirichlet process model for survival analysis data. A major feature
of the proposed approach is that there is no necessity for resulting survival curve estimates …