Analysis of Incomplete Multivariate Data (Chapman & Hall/CRC Monographs on Statistics and Applied Probability) This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limited signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well maintained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping. .

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Bibliografische Details

Titel: Analysis of Incomplete Multivariate Data (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)
Verlag: Chapman and Hall/CRC
Erscheinungsjahr: 1997
Sprache: Englisch
ISBN-10: 0412040611
ISBN-13: 9780412040610
Einband: Hardcover
Zustand: Very Good

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Inhaltsangabe

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.

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J.L. Schafer

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