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During the last decades, there has been an explosion in computation and information technology. This development comes with an expansion of complex observational studies and clinical trials in a variety of fields such as medicine, biology, epidemiology, sociology, and economics among many others, which involve collection of large amounts of data on subjects or organisms over time. This book provides a fundamental statistical framework for the analysis of this type of data.
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Recensione
From the reviews:
"This book provides a rigourous statistical framework for the analysis of complex large longitudinal data. It provides a comprehensive description of optimal estimation techniques based on time-dependent data structures ... . This is an excellent book for Ph.D. level students in Biostatistics and Statistics who have a strong background in mathematics. It is also suitable for researchers in statistics with a strong interest in the analysis of complex longitudinal data." (Subhash C. Kochar, Sankhya: The Indian Journal of Statistics, Vol. 66 (1), 2004)
"This book provides a fundamental statistical framework for the analysis of complex longitudinal data. It provides the first comprehensive description of optimal estimation techniques based on time-dependent data structures ... . The book can be used to teach masters-level and Ph.D. students in biostatistics and statistics and is suitable for researchers in statistics with a strong interest in the analysis of complex longitudinal data." (P. Rochus, Mathematical Reviews, 2003m)
"This book by two major research workers in the field addresses in generality important problems involving multivariate longitudinal data ... . it is an important book dealing with important problems. Therefore, experts in modern semi-parametric theory should certainly read the book. Those with an interest focussed more on applications and able to draw together a reading group with appropriate expertise are very likely to profit greatly from a sustained study of the book." (D.R. Cox, Short Book Reviews, Vol. 23 (2), 2003)
Contenuti
Introduction * General Methodology * Monotone Censored Data * Cross Sectional Data and Right Censored Data Combined * Multivariate Right Censored Multivariate Data * Unified Approach for Causal Inference and Censored Data
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Unified Methods for Censored Longitudinal Data and Causality (Springer Series in Statistics)
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Unified Methods for Censored Longitudinal Data and Causality (Springer Series in Statistics)
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Unified Methods for Censored Longitudinal Data and Causality (Springer Series in Statistics)
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Unified Methods for Censored Longitudinal Data and Causality
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ISBN 13: 9781441930552
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -A fundamental statistical framework for the analysis of complex longitudinal data is provided in this book. It provides the first comprehensive description of optimal estimation techniques based on time-dependent data structures. The techniques go beyond standard statistical approaches and can be used to teach masters and Ph.D. students. The text is ideally suitable for researchers in statistics with a strong interest in the analysis of complex longitudinal data. 412 pp. Englisch. Codice articolo 9781441930552
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. During the last decades, there has been an explosion in computation and information technology. This development comes with an expansion of complex observational studies and clinical trials in a variety of fields such as medicine, biology, epidemiology, soc. Codice articolo 4173516
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Unified Methods for Censored Longitudinal Data and Causality
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Springer New York, Springer US Mai 2011, 2011
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ISBN 13: 9781441930552
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Taschenbuch. Condizione: Neu. Neuware -During the last decades, there has been an explosion in computation and information technology. This development comes with an expansion of complex observational studies and clinical trials in a variety of fields such as medicine, biology, epidemiology, sociology, and economics among many others, which involve collection of large amounts of data on subjects or organisms over time. The goal of such studies can be formulated as estimation of a finite dimensional parameter of the population distribution corresponding to the observed time- dependent process. Such estimation problems arise in survival analysis, causal inference and regression analysis. This book provides a fundamental statistical framework for the analysis of complex longitudinal data. It provides the first comprehensive description of optimal estimation techniques based on time-dependent data structures subject to informative censoring and treatment assignment in so called semiparametric models. Semiparametric models are particularly attractive since they allow the presence of large unmodeled nuisance parameters. These techniques include estimation of regression parameters in the familiar (multivariate) generalized linear regression and multiplicative intensity models. They go beyond standard statistical approaches by incorporating all the observed data to allow for informative censoring, to obtain maximal efficiency, and by developing estimators of causal effects. It can be used to teach masters and Ph.D. students in biostatistics and statistics and is suitable for researchers in statistics with a strong interest in the analysis of complex longitudinal data.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 412 pp. Englisch. Codice articolo 9781441930552
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Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - During the last decades, there has been an explosion in computation and information technology. This development comes with an expansion of complex observational studies and clinical trials in a variety of fields such as medicine, biology, epidemiology, sociology, and economics among many others, which involve collection of large amounts of data on subjects or organisms over time. The goal of such studies can be formulated as estimation of a finite dimensional parameter of the population distribution corresponding to the observed time- dependent process. Such estimation problems arise in survival analysis, causal inference and regression analysis. This book provides a fundamental statistical framework for the analysis of complex longitudinal data. It provides the first comprehensive description of optimal estimation techniques based on time-dependent data structures subject to informative censoring and treatment assignment in so called semiparametric models. Semiparametric models are particularly attractive since they allow the presence of large unmodeled nuisance parameters. These techniques include estimation of regression parameters in the familiar (multivariate) generalized linear regression and multiplicative intensity models. They go beyond standard statistical approaches by incorporating all the observed data to allow for informative censoring, to obtain maximal efficiency, and by developing estimators of causal effects. It can be used to teach masters and Ph.D. students in biostatistics and statistics and is suitable for researchers in statistics with a strong interest in the analysis of complex longitudinal data. Codice articolo 9781441930552
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