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Da: moluna, Greven, Germania
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Aggiungi al carrelloCondizione: New. Trivellore Raghunathan is the director of the Survey Research Center in the Institute for Social Research and professor of biostatistics in the School of Public Health at the University of Michigan. He has published numerous papers in a .
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Missing Data Analysis in Practice | Trivellore Raghunathan | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2020 | Taylor & Francis | EAN 9780367737665 | 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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Lingua: Inglese
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ISBN 10: 0367737663 ISBN 13: 9780367737665
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Missing Data Analysis in Practice provides practical methods for analyzing missing data along with the heuristic reasoning for understanding the theoretical underpinnings. Drawing on his 25 years of experience researching, teaching, and consulting in quantitative areas, the author presents both frequentist and Bayesian perspectives. He describes easy-to-implement approaches, the underlying assumptions, and practical means for assessing these assumptions. Actual and simulated data sets illustrate important concepts, with the data sets and codes available online.The book underscores the development of missing data methods and their adaptation to practical problems. It mainly focuses on the traditional missing data problem. The author also shows how to use the missing data framework in many other statistical problems, such as measurement error, finite population inference, disclosure limitation, combing information from multiple data sources, and causal inference. 230 pp. Englisch.
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Lingua: Inglese
Editore: Taylor & Francis, Chapman And Hall/CRC, 2020
ISBN 10: 0367737663 ISBN 13: 9780367737665
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Missing Data Analysis in Practice provides practical methods for analyzing missing data along with the heuristic reasoning for understanding the theoretical underpinnings. Drawing on his 25 years of experience researching, teaching, and consulting in quantitative areas, the author presents both frequentist and Bayesian perspectives. He describes easy-to-implement approaches, the underlying assumptions, and practical means for assessing these assumptions. Actual and simulated data sets illustrate important concepts, with the data sets and codes available online.The book underscores the development of missing data methods and their adaptation to practical problems. It mainly focuses on the traditional missing data problem. The author also shows how to use the missing data framework in many other statistical problems, such as measurement error, finite population inference, disclosure limitation, combing information from multiple data sources, and causal inference.
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Aggiungi al carrelloGebunden. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Trivellore Raghunathan is the director of the Survey Research Center in the Institute for Social Research and professor of biostatistics in the School of Public Health at the University of Michigan. He has published numerous papers in a .
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 151,20
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Aggiungi al carrelloBuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book provides practical methods for analyzing missing data along with the heuristic reasoning for understanding the theoretical underpinnings. The author presents both frequentist and Bayesian perspectives. He describes easy-to-implement approaches, the underlying assumptions, and practical means for assessing these assumptions. Actual and simulated data sets illustrate important concepts, with the data sets and codes available online. The book also includes practical examples, simulation studies, projects, and end-of-chapter exercises.