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Published by Chapman and Hall/CRC, 1997
ISBN 10: 0412040611 ISBN 13: 9780412040610
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Published by Chapman and Hall/CRC, 1997
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Published by Chapman and Hall/CRC, 1997
ISBN 10: 0412040611 ISBN 13: 9780412040610
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Published by Chapman and Hall/CRC, 1997
ISBN 10: 0412040611 ISBN 13: 9780412040610
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Published by Chapman and Hall/CRC, 2023
ISBN 10: 1032477997 ISBN 13: 9781032477992
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Published by Chapman and Hall/CRC, 2023
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Published by Chapman and Hall/CRC, 2023
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Taschenbuch. Condition: Neu. Analysis of Incomplete Multivariate Data | J.L. Schafer | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2023 | Taylor & Francis | EAN 9781032477992 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
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Published by Taylor & Francis Ltd, 1997
ISBN 10: 0412040611 ISBN 13: 9780412040610
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Language: English
Published by Chapman and Hall/CRC 1997-08-01, 1997
ISBN 10: 0412040611 ISBN 13: 9780412040610
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Published by Chapman and Hall/CRC, 1997
ISBN 10: 0412040611 ISBN 13: 9780412040610
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Published by Chapman and Hall/CRC, 1997
ISBN 10: 0412040611 ISBN 13: 9780412040610
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Language: English
Published by Chapman and Hall/CRC, 1997
ISBN 10: 0412040611 ISBN 13: 9780412040610
Seller: GreatBookPrices, Columbia, MD, U.S.A.
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Language: English
Published by Chapman and Hall/CRC, 1997
ISBN 10: 0412040611 ISBN 13: 9780412040610
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Condition: New. J.L. SchaferThe 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 .
Language: English
Published by Taylor & Francis Ltd, 1997
ISBN 10: 0412040611 ISBN 13: 9780412040610
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Published by Taylor & Francis Ltd Aug 1997, 1997
ISBN 10: 0412040611 ISBN 13: 9780412040610
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Buch. Condition: Neu. Neuware - This work develops simulation techniques based on Bayesian (subjective) inference for multivariate data with missing values.
Language: English
Published by Taylor & Francis, Chapman And Hall/CRC, 2023
ISBN 10: 1032477997 ISBN 13: 9781032477992
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 444 pp. Englisch.
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. J.L. SchaferThe 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 tool.
Language: English
Published by Taylor & Francis, Chapman And Hall/CRC, 2023
ISBN 10: 1032477997 ISBN 13: 9781032477992
Seller: AHA-BUCH GmbH, Einbeck, Germany
Taschenbuch. Condition: Neu. 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.