Bayesian Missing Data Problems by Tan Ming (26 results)

Language: English
Published by Chapman and Hall/CRC 2019
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Language: English
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Language: English
Published by Chapman and Hall/CRC 2019
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Published by Taylor & Francis Ltd 2019
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Language: English
Published by Chapman and Hall/CRC 2019
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Language: English
Published by Chapman and Hall/CRC 2019
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Published by Chapman and Hall/CRC 2019
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Language: English
Published by Chapman and Hall/CRC 2009
Series: Chapman & Hall/CRC Biostatistics, Book 15 of 155. Book 15 of 155 - Chapman & Hall/CRC Biostatistics
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Published by Chapman & Hall 2019
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Language: English
Published by Chapman and Hall/CRC 2019
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Language: English
Published by Chapman and Hall/CRC 2009
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Language: English
Published by Taylor & Francis Group 2009
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Condition: New. pp. 344 Index.

Language: English
Published by Chapman and Hall/CRC 2009
Series: Chapman & Hall/CRC Biostatistics, Book 15 of 155. Book 15 of 155 - Chapman & Hall/CRC Biostatistics
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Language: English
Published by Taylor & Francis Group 2009
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Language: English
Published by CRC Press 2009
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Condition: New. Ming T. Tan is Professor of Biostatistics in the Department of Epidemiology and Preventive Medicine at the University of Maryland School of Medicine and Director of the Division of Biostatistics at the University of Maryland Greenebaum C.

Language: English
Published by Chapman and Hall/CRC 2009
Series: Chapman & Hall/CRC Biostatistics, Book 15 of 155. Book 15 of 155 - Chapman & Hall/CRC Biostatistics
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Language: English
Published by Chapman and Hall/CRC 2009
Series: Chapman & Hall/CRC Biostatistics, Book 15 of 155. Book 15 of 155 - Chapman & Hall/CRC Biostatistics
- Hardcover
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Language: English
Published by Taylor & Francis Ltd Aug 2009 2009
Series: Chapman & Hall/CRC Biostatistics, Book 15 of 155. Book 15 of 155 - Chapman & Hall/CRC Biostatistics
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Buch. Condition: Neu. Neuware - This book provides a systematic view of Bayesian methods to be used with missing data problems. The text presents a non-iterative approach that provides either explicit or non-iterative sampling calculation of posteriors. This computation includes exact numerical solutions, a conditional sampling…approach via data augmentation, and a non-iterative sampling approach via EM-type algorithms. It illustrates the application of Bayesian analysis to important biostatistics problems and to other real-world applications, including the constrained parameter problem reformulated as a missing data problem. The text includes S-PLUS/R computer codes to supplement existing functions for statistical distributions and stochastic processes.

Language: English
Published by CRC Press 2019
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Language: English
Published by CRC Press 2019
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Language: English
Published by Chapman and Hall/CRC 2019
Series: Chapman & Hall/CRC Biostatistics, Book 15 of 155. Book 15 of 155 - Chapman & Hall/CRC Biostatistics
- Softcover
- Print on Demand
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Language: English
Published by CRC Press 2019
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Ming T. Tan is Professor of Biostatistics in the Department of Epidemiology and Preventive Medicine at the University of Maryland School of Medicine and Director of the Division of Biostatistics at the University of M…aryland Greenebaum C.

Language: English
Published by Taylor & Francis Group 2009
Series: Chapman & Hall/CRC Biostatistics, Book 15 of 155. Book 15 of 155 - Chapman & Hall/CRC Biostatistics
- Hardcover
- Print on Demand
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Condition: New. pp. 344 This item is printed on demand.

Language: English
Published by Chapman And Hall/CRC 2019
Series: Chapman & Hall/CRC Biostatistics, Book 15 of 155. Book 15 of 155 - Chapman & Hall/CRC Biostatistics
- Softcover
- Print on Demand
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book presents solutions to missing data problems through explicit or noniterative sampling calculation of Bayesian posteriors, based on the inverse Bayes formulae. The authors focus on exact numerical solutions, a conditional sampli…ng approach via data augmentation, and a noniterative sampling approach via EM-type algorithms. They describe Monte Carlo simulation, numerical techniques, and optimization methods. The book illustrates the methods with biostatistical models and real-world applications, including mixed effects and hierarchical models, nonresponse and contingency tables, and the constrained parameter problem reformulated as a missing data problem.