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Predictive Inference

Inglese · Copertina rigida

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Descrizione

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The author's research has been directed towards inference involving observables rather than parameters. In this book, he brings together his views on predictive or observable inference and its advantages over parametric inference. While the book discusses a variety of approaches to prediction including those based on parametric, nonparametric, and nonstochastic statistical models, it is devoted mainly to predictive applications of the Bayesian approach. It not only substitutes predictive analyses for parametric analyses, but it also presents predictive analyses that have no real parametric analogues. It demonstrates that predictive inference can be a critical component of even strict parametric inference when dealing with interim analyses. This approach to predictive inference will be of interest to statisticians, psychologists, econometricians, and sociologists.

Info autore

Geisser\, Seymour

Riassunto

This book presents Seymour Geisser's views on predictive or observable inference and its advantages over parametric inference. It focuses on the predictive applications of the Bayesian approach. The book also presents predictive analyses that have no real parametric analogues.

Testo aggiuntivo

"...this monograph is a very welcome attempt to shift back the main emphasis of statistics from parametric estimation and testing to prediction which, as noted by the author, was originally the earliest and most prealent form of statistical inference...I am sure all statisticians and students of statistics with an open mind will enjoy reading it and, hopefully will appreciate the beauty and usefulness of a coherent predictive view of their subject."-Mathematical Reviews"Predictive Inference: An Introduction is rich both in the coverage of topics and in applications...The monograph is addressed to statisticians and research workers who are intrested in the predictive approach. Its major contribution is likely to be as a resource for persons interested in trying predictive inference in some application."-Journal of the ASA

Relazione

"...this monograph is a very welcome attempt to shift back the main emphasis of statistics from parametric estimation and testing to prediction which, as noted by the author, was originally the earliest and most prealent form of statistical inference...I am sure all statisticians and students of statistics with an open mind will enjoy reading it and, hopefully will appreciate the beauty and usefulness of a coherent predictive view of their subject."
-Mathematical Reviews

"Predictive Inference: An Introduction is rich both in the coverage of topics and in applications...The monograph is addressed to statisticians and research workers who are intrested in the predictive approach. Its major contribution is likely to be as a resource for persons interested in trying predictive inference in some application."
-Journal of the ASA

Dettagli sul prodotto

Con la collaborazione di Howell Tong (Editore), D. R. Cox (Editore), Thomas A. Louis (Editore), Valerie Isham (Editore), N. Reid (Editore), Niels Keiding (Editore), R.J. Tibshirani (Editore), Seymour Gkisser (Editore), Howell Tong (Editore della collana), Thomas A. Louis (Editore della collana), Valerie Isham (Editore della collana), N. Reid (Editore della collana), Niels Keiding (Editore della collana), D.R. Cox (Editore della collana), R.J. Tibshirani (Editore della collana)
Autori Seymour Geisser, Geisser Geisser, Seymour (University of Minnesota) Geisser, Geisser Seymour
Editore Springer Netherlands
 
Contenuto Libro
Forma del prodotto Copertina rigida
Data pubblicazione 17.04.2014
Categoria Scienze naturali, medicina, informatica, tecnica > Matematica > Teoria delle probabilità, stocastica, statistica m
 
EAN 9780412034718
ISBN 978-0-412-03471-8
Numero di pagine 276
Dimensioni (della confezione) 21 x 28 cm
Peso (della confezione) 359 g
 
Serie Tertiary Level Biology > 55, Monographs on Statistics and Applied Probability > Vol.55, Chapman & Hall/CRC Monographs on Statistics & Applied Probability > 55, Chapman & Hall/CRC Monographs on Statistics & Applied Probability > 55, Chapman & Hall/CRC Monographs on Statistics and Applied Probability, Chapman & Hall/CRC Monographs > 55
Categorie MATHEMATICS / Probability & Statistics / General, process optimization, Probability & statistics, Probability and statistics, Multivariate Analysis, Distribution Function, bayesian statistics, Loss Function, Data Set, Data Sets, Posterior Distribution, Aid Virus, interim data analysis, predictive methods in social sciences, statistical model selection, nonparametric models, Interim Analyses, Hellinger Distance, Posterior Odds, Confidence Coefficient, HPD interval, Prior Density, De Finetti’s Theorem, Measurement Error Model, Binary Diagnostic Test, Kullback Divergence, Highest Probability Density Regions, Predictive Confidence Interval, Conditional Predictive Density, Discordancy Indices, Predictive Distribution Function
 

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