Analysis of Incomplete Multivariate Data

J.L. Schafer, D.R. Cox (Nuffield College, Oxford University, UK), N. Reid (University of Toronto, Ontario, Canada) , Valerie Isham

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English

CRC Press
21 January 2023

Probability & statistics; Applied mathematics

Series: Chapman & Hall/CRC Monographs on Statistics and Applied Probability

The last two decades have seen enormous developments in statistical methods for incomplete data. The EM algorithm and its extensions, multiple imputation, and Markov Chain Monte Carlo provide a set of flexible and reliable tools from inference in large classes of missing-data problems. Yet, in practical terms, those developments have had surprisingly little impact on the way most data analysts handle missing values on a routine basis.

Analysis of Incomplete Multivariate Data helps bridge the gap between theory and practice, making these missing-data tools accessible to a broad audience. It presents a unified, Bayesian approach to the analysis of incomplete multivariate data, covering datasets in which the variables are continuous, categorical, or both. The focus is applied, where necessary, to help readers thoroughly understand the statistical properties of those methods, and the behavior of the accompanying algorithms.

All techniques are illustrated with real data examples, with extended discussion and practical advice. All of the algorithms described in this book have been implemented by the author for general use in the statistical languages S and S Plus. The software is available free of charge on the Internet.

By: J.L. Schafer
Series edited by: D.R. Cox (Nuffield College Oxford University UK), N. Reid (University of Toronto, Ontario, Canada), Valerie Isham, Thomas A. Louis (Johns Hopkins Bloomberg School of Public Health, MD, USA Johns Hopkins Bloomberg School of Public Health, Baltimore, Johns Hopkins Bloomberg, Baltimore, Maryland, USA)
Imprint: CRC Press
Country of Publication: United Kingdom
Dimensions: Height: 216mm, Width: 138mm,
Weight: 821g
ISBN: 9781032477992
ISBN 10: 1032477997
Series: Chapman & Hall/CRC Monographs on Statistics and Applied Probability
Pages: 444
Publication Date: 21 January 2023
Audience: College/higher education , Primary
Format: Paperback
Publisher's Status: Active

Introduction Assumptions EM and Inference by Data Augmentation Methods for Normal Data More on the Normal Model Methods for Categorical Data Loglinear Models Methods for Mixed Data Further Topics Appendices References Index

J.L. Schafer

Reviews for Analysis of Incomplete Multivariate Data

"""Overall, the book provides a sound basis on which one can build when dealing with real data…I take pleasure in recommending this well-written text."" -Rainer Schlittgen in Statistical Papers ""This book provides an excellent introduction to statistical inference…Thanks to the clear and relatively complete treatment of many of the main ideas in this area, even theoretically oriented readers may find this book worthwhile."" -Mark Steel, Mathematical Reviews"