About this product

Product Identifiers

PublisherCambridge University Press

ISBN-100521852250

ISBN-139780521852258

eBay Product ID (ePID)65898015

Product Key Features

Number of Pages332 Pages

Publication NameModel Selection and Model Averaging

LanguageEnglish

Publication Year2008

SubjectProbability & Statistics / General, General, Probability & Statistics / Bayesian Analysis

TypeTextbook

AuthorNils Lid Hjort, Gerda Claeskens

Subject AreaMathematics

SeriesCambridge Series in Statistical and Probabilistic Mathematics Ser.

FormatHardcover

Dimensions

Item Height1 in

Item Weight30.8 Oz

Item Length10.2 in

Item Width7.2 in

Additional Product Features

Intended AudienceScholarly & Professional

LCCN2008-006507

Reviews'... given the inviting style of the presentation and the quality of the material, this book could be quite a catch for graduate students as well as for practitioners where models really do make [a] difference.' MAA Reviews, 'â€¦ given the inviting style of the presentation and the quality of the material, this book could be quite a catch for graduate students as well as for practitioners where models really do make [a] difference.' MAA Reviews, "'This is a good textbook for a master-level statistical course about model selection.' It covers many important concepts and methods about model selection." Mathematical Reviews, '... the authors have succeeded in bringing together a coherent volume, which gives a state of the art account of the current practice in model selection and comparison, containing a plethora of asymptotic (sometimes new) results, which can be used to compare different model choice criteria. Most importantly, this is the sole volume dedicated to this subject, taking a fully statistical as opposed to an information theoretic approach to the topic of model selection.' Statistics in Society, 'This is a good textbook for a master-level statistical course about model selection.' Mathematical Reviews, 'â€¦ the authors have succeeded in bringing together a coherent volume, which gives a state of the art account of the current practice in model selection and comparison, containing a plethora of asymptotic (sometimes new) results, which can be used to compare different model choice criteria. Most importantly, this is the sole volume dedicated to this subject, taking a fully statistical as opposed to an information theoretic approach to the topic of model selection.' Statistics in Society, "This book is comprehensive in its treatment of the subject and will probably teach something new, even to the most experienced researchers in model selection. The authors have succeeded in bringing together a coherent volume, which gives a state of the art account of the current practice in model selection and comparison, containing a plethora of asymptotic (sometimes new) results, which can be used to compare different model choice criteria. Most importantly, this is the sole volume dedicated to this subject, taking a fully statistical as opposed to an information theoretic approach to the topic of model selection. This book will be attractive to a wide range of graduate students and researchers, users or developers of model choice criteria, of all statistical persuasions." Cedric E. Ginestet, Statistics in Society, "Overall, given the inviting style of the presentation and the quality of the material, this book could be quite a catch for graduate students as well as for practitioners where models really do make a difference." Ita Cirovic Donev, MAA Reviews, "This book is the best available review of model selection from a statistical standpoint. It has a very nice combination of just-enough statistical theory with lots of non-trivial worked examples, and the theory is well-presented and useful, without much being left to folklore." Cosma Shalizi, The Bactra Review, "All data analyses are compatible with open-source R software, and data sets and R code are available from a companion web site." Book News

Dewey Edition22

Series Volume NumberSeries Number 27

IllustratedYes

Dewey Decimal519.5

Table Of ContentPreface; A guide to notation; 1. Model selection: data examples and introduction; 2. Akaike's information criterion; 3. The Bayesian information criterion; 4. A comparison of some selection methods; 5. Bigger is not always better; 6. The focussed information criterion; 7. Frequentist and Bayesian model averaging; 8. Lack-of-fit and goodness-of-fit tests; 9. Model selection and averaging schemes in action; 10. Further topics; Overview of data examples; Bibliography; Author index; Subject index.

SynopsisChoosing a model is central to all statistical work with data. We have seen rapid advances in model fitting and in the theoretical understanding of model selection, yet this book is the first to synthesize research and practice from this active field. Model choice criteria are explained, discussed and compared, including the AIC, BIC, DIC and FIC. The uncertainties involved with model selection are tackled with discussions of frequent and Bayesian methods; model averaging schemes are presented. Real-data examples are complemented by derivations providing deeper insight into the methodology, and instructive exercises build familiarity with the methods. The companion website features Data sets and R-code., Given a data set, you can fit thousands of models at the push of a button, but how do you choose the best? With so many candidate models, overfitting is a real danger. Is the monkey who typed Hamlet actually a good writer? Choosing a model is central to all statistical work with data. We have seen rapid advances in model fitting and in the theoretical understanding of model selection, yet this book is the first to synthesize research and practice from this active field. Model choice criteria are explained, discussed and compared, including the AIC, BIC, DIC and FIC. The uncertainties involved with model selection are tackled, with discussions of frequentist and Bayesian methods; model averaging schemes are presented. Real-data examples are complemented by derivations providing deeper insight into the methodology, and instructive exercises build familiarity with the methods. The companion website features Data sets and R code., Choosing a model is central to all statistical work with data; this book is the first to synthesize research and practice from this active field. Model choice criteria are explained, discussed and compared, including the AIC, BIC, DIC and FIC. Real-data examples and exercises build familiarity with the methods.

LC Classification NumberQA276.18.C53 2008