This review covers recent results concerning the estimation of large covariance matrices
using tools from Random Matrix Theory (RMT). We introduce several RMT methods and …

This paper is divided into two main sections. The first half of the paper focuses on the intent
and practice of diagnostic assessment, providing a general organizing scheme for a …

INTRODUCTION Governments across the world have implemented a wide range of
nonpharmaceutical interventions (NPIs) to mitigate the spread of severe acute respiratory …

General state-space Markov chain theory has evolved to make it both more accessible and
more powerful. Markov Chain Monte Carlo in Practice introduces MCMC methods and their …

Group-level variance estimates of zero often arise when fitting multilevel or hierarchical
linear models, especially when the number of groups is small. For situations where zero …

This paper considers Bayesian counterparts of the classical tests for goodness of fit and their
use in judging the fit of a single Bayesian model to the observed data. We focus on posterior …

A hands-on introduction to the principles of Bayesian modeling using WinBUGS Bayesian
Modeling Using WinBUGS provides an easily accessible introduction to the use of …

Since our first edition of this book, many developments in statistical mod elling based on
generalized linear models have been published, and our primary aim is to bring the book up …

Praise for the First Edition" This pioneering work, in which Rao provides a comprehensive
and up-to-date treatment of small area estimation, will become a classic... I believe that it has …

An accessible introduction to indirect estimation methods, both traditional and model-based.
Readers will also find the latest methods for measuring the variability of the estimates as …