Analysis of Incomplete Multivariate Data

J.L. Schafer

ISBN 10: 1032477997 / ISBN 13: 9781032477992

Published by Taylor & Francis, Chapman And Hall/CRC, 2023

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - 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.

Seller Inventory # 9781032477992

Bibliographic Details

• Title: Analysis of Incomplete Multivariate Data
• Publisher: Taylor & Francis, Chapman And Hall/CRC
• Publication Date: 2023
• Language: English
• ISBN 10: 1032477997
• ISBN 13: 9781032477992
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 216W x 138H x 24D millimeters 216 millimeters width by 138 millimeters height by 24 millimeters depth
• Weight: 453 grams

About this title

Synopsis

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.

About the Author

J.L. Schafer