Description du livre Etat : New. New! This book is in the same immaculate condition as when it was published. N� de r�f. du vendeur 353-1441918256-new

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Partial Identification of Probability Distributions

Charles F. Manski

Edit� par Springer New York Dez 2011 (2011)

ISBN 10 : 1441918256 ISBN 13 : 9781441918253

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Description du livre Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Sample data alone never suffice to draw conclusions about populations. Inference always requires assumptions about the population and sampling process. Statistical theory has revealed much about how strength of assumptions affects the precision of point estimates, but has had much less to say about how it affects the identification of population parameters. Indeed, it has been commonplace to think of identification as a binary event- a parameter is either identified or not- and to view point identification as a precondition for inference. Yet there is enormous scope for fruitful inference using data and assumptions that partially identify population parameters. This book explains why and shows how. The book presents in a rigorous and thorough manner the main elements of Charles Manski's research on partial identification of probability distributions. One focus is prediction with missing outcome or covariate data. Another is decomposition of finite mixtures, with application to the analysis of contaminated sampling and ecological inference. A third major focus is the analysis of treatment response. Whatever the particular subject under study, the presentation follows a common path. The author first specifies the sampling process generating the available data and asks what may be learned about population parameters using the empirical evidence alone. He then ask how the (typically) setvalued identification regions for these parameters shrink if various assumptions are imposed. The approach to inference that runs throughout the book is deliberately conservative and thoroughly nonparametric. 196 pp. Englisch. N� de r�f. du vendeur 9781441918253

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Partial Identification of Probability Distributions

Charles F. Manski

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ISBN 10 : 1441918256 ISBN 13 : 9781441918253

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Description du livre Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The book presents in a rigorous and thorough manner the main elements of Charles Manski s research on partial identification of probability distributions. The approach to inference that runs throughout the book is deliberately conservative and thoroughly. N� de r�f. du vendeur 4172401

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Partial Identification of Probability Distributions

Charles F. Manski

Edit� par Springer-Verlag New York Inc. (2011)

ISBN 10 : 1441918256 ISBN 13 : 9781441918253

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Description du livre Paperback / softback. Etat : New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days. N� de r�f. du vendeur C9781441918253

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Partial Identification of Probability Distributions

Charles F. Manski

Edit� par Springer New York (2011)

ISBN 10 : 1441918256 ISBN 13 : 9781441918253

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Description du livre Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Sample data alone never suffice to draw conclusions about populations. Inference always requires assumptions about the population and sampling process. Statistical theory has revealed much about how strength of assumptions affects the precision of point estimates, but has had much less to say about how it affects the identification of population parameters. Indeed, it has been commonplace to think of identification as a binary event- a parameter is either identified or not- and to view point identification as a precondition for inference. Yet there is enormous scope for fruitful inference using data and assumptions that partially identify population parameters. This book explains why and shows how. The book presents in a rigorous and thorough manner the main elements of Charles Manski's research on partial identification of probability distributions. One focus is prediction with missing outcome or covariate data. Another is decomposition of finite mixtures, with application to the analysis of contaminated sampling and ecological inference. A third major focus is the analysis of treatment response. Whatever the particular subject under study, the presentation follows a common path. The author first specifies the sampling process generating the available data and asks what may be learned about population parameters using the empirical evidence alone. He then ask how the (typically) setvalued identification regions for these parameters shrink if various assumptions are imposed. The approach to inference that runs throughout the book is deliberately conservative and thoroughly nonparametric. N� de r�f. du vendeur 9781441918253

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