Missing data, a common but challenging issue in most studies, may lead to biased and
inefficient inferences if handled inappropriately. As a natural and powerful way for dealing …

Several topics are addressed, including parametric models, semiparametric models based
on prior processes, proportional and non-proportional hazards models, frailty models, cure …

To date, several methods exist in the statistical literature for model assessment, which
purport themselves specifically as Bayesian predictive methods. The decision theoretic …

Sampling from the posterior distribution and computing posterior quanti ties of interest using
Markov chain Monte Carlo (MCMC) samples are two major challenges involved in advanced …

This book provides an accessible approach to Bayesian computing and data analysis, with
an emphasis on the interpretation of real data sets. Following in the tradition of the …

This volume examines modern techniques and research problems in the analysis of lifetime
data analysis. This area of statistics deals with time-to-event data which is complicated not …

This book reviews the state-of-the-art advances in skew-elliptical distributions and provides
many new developments in a single volume, collecting theoretical results and applications …

The use of Markov chain Monte Carlo (MCMC) methods for estimating hierarchical models
involves complex data structures and is often described as a revolutionary development. An …

Thoroughly updated throughout, A First Course in Linear Model Theory, Second Edition is
an intermediate-level statistics text that fills an important gap by presenting the theory of …

An intermediate-level treatment of Bayesian hierarchical models and their applications, this
book demonstrates the advantages of a Bayesian approach to data sets involving inferences …