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Sinossi
Statisticians and applied scientists often must select a model to fit empirical data. This book introduces researchers and graduate students in many areas to an information criterion approach, first introduced by Hirotugu Akaike in 1973. The book will be of general interest, but the emphasis is on applications to the biological sciences.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Contenuti
Introduction * Information and Likelihood Theory: A Basis for Model Selection and Inference * Basic Use of the Information-Theoretic Approach * Formal Inference From More Than One Model: Multi-Model Inference (MMI) * Monte Carlo Insights and Extended Examples * Statistical Theory and Numerical Results * Summary
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
- EditoreSpringer
- Data di pubblicazione2010
- ISBN 10 1441929738
- ISBN 13 9781441929730
- RilegaturaCopertina flessibile
- LinguaInglese
- Numero edizione2
- Numero di pagine520
- Contatto del produttorenon disponibile
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Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach
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Model Selection and Multimodel Inference
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Includes supplementary material: sn.pub/extrasA unique and comprehensive text on the philosophy of model-based data analysis and strategy for the analysis of empirical data. The book introduces information theoretic approaches and focuses critica. Codice articolo 4173440
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Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach
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Springer (edition ), 2010
ISBN 10: 1441929738
ISBN 13: 9781441929730
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Model Selection and Multimodel Inference
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Springer New York, Springer US Dez 2010, 2010
ISBN 10: 1441929738
ISBN 13: 9781441929730
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -We wrote this book to introduce graduate students and research workers in various scienti c disciplines to the use of information-theoretic approaches in the analysis of empirical data. These methods allow the data-based selection of a 'best' model and a ranking and weighting of the remaining models in a pre-de ned set. Traditional statistical inference can then be based on this selected best model. However, we now emphasize that information-theoretic approaches allow formal inference to be based on more than one model (m- timodel inference). Such procedures lead to more robust inferences in many cases, and we advocate these approaches throughout the book. The second edition was prepared with three goals in mind. First, we have tried to improve the presentation of the material. Boxes now highlight ess- tial expressions and points. Some reorganization has been done to improve the ow of concepts, and a new chapter has been added. Chapters 2 and 4 have been streamlined in view of the detailed theory provided in Chapter 7. S- ond, concepts related to making formal inferences from more than one model (multimodel inference) have been emphasized throughout the book, but p- ticularly in Chapters 4, 5, and 6. Third, new technical material has been added to Chapters 5 and 6. Well over 100 new references to the technical literature are given. These changes result primarily from our experiences while giving several seminars, workshops, and graduate courses on material in the rst e- tion. 520 pp. Englisch. Codice articolo 9781441929730
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Model Selection and Multimodel Inference
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Springer New York, Springer US Dez 2010, 2010
ISBN 10: 1441929738
ISBN 13: 9781441929730
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Taschenbuch. Condizione: Neu. Neuware -We wrote this book to introduce graduate students and research workers in various scienti c disciplines to the use of information-theoretic approaches in the analysis of empirical data. These methods allow the data-based selection of a ¿best¿ model and a ranking and weighting of the remaining models in a pre-de ned set. Traditional statistical inference can then be based on this selected best model. However, we now emphasize that information-theoretic approaches allow formal inference to be based on more than one model (m- timodel inference). Such procedures lead to more robust inferences in many cases, and we advocate these approaches throughout the book. The second edition was prepared with three goals in mind. First, we have tried to improve the presentation of the material. Boxes now highlight ess- tial expressions and points. Some reorganization has been done to improve the ow of concepts, and a new chapter has been added. Chapters 2 and 4 have been streamlined in view of the detailed theory provided in Chapter 7. S- ond, concepts related to making formal inferences from more than one model (multimodel inference) have been emphasized throughout the book, but p- ticularly in Chapters 4, 5, and 6. Third, new technical material has been added to Chapters 5 and 6. Well over 100 new references to the technical literature are given. These changes result primarily from our experiences while giving several seminars, workshops, and graduate courses on material in the rst e- tion.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 520 pp. Englisch. Codice articolo 9781441929730
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Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach
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