Model selection and model averaging:
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 r...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2008
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| Schriftenreihe: | Cambridge series on statistical and probabilistic mathematics
27 |
| Schlagworte: | |
| Online-Zugang: | DE-12 DE-92 Volltext |
| Zusammenfassung: | 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 |
| Beschreibung: | Title from publisher's bibliographic system (viewed on 05 Oct 2015) |
| Beschreibung: | 1 online resource (xvii, 312 pages) |
| ISBN: | 9780511790485 |
| DOI: | 10.1017/CBO9780511790485 |
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| contents | Model selection : data examples and introduction -- Akaike's information criterion -- The Bayesian information criterion -- A comparison of some selection methods -- Bigger is not always better -- The focussed information criterion -- Frequentist and Bayesian model averaging -- Lack-of-fit and goodness-of-fit tests -- Model selection and averaging schemes in action |
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| dewey-full | 519.5 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
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| dewey-search | 519.5 |
| dewey-sort | 3519.5 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik Wirtschaftswissenschaften |
| doi_str_mv | 10.1017/CBO9780511790485 |
| format | Electronic eBook |
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| id | DE-604.BV043940549 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-06T09:04:03Z |
| institution | BVB |
| isbn | 9780511790485 |
| language | English |
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| publishDate | 2008 |
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| publisher | Cambridge University Press |
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| series2 | Cambridge series on statistical and probabilistic mathematics |
| spelling | Claeskens, Gerda 1973- Verfasser (DE-588)1147083088 aut Model selection and model averaging Gerda Claeskens, Nils Lid Hjort Model Selection & Model Averaging Cambridge Cambridge University Press 2008 1 online resource (xvii, 312 pages) txt rdacontent c rdamedia cr rdacarrier Cambridge series on statistical and probabilistic mathematics 27 Title from publisher's bibliographic system (viewed on 05 Oct 2015) Model selection : data examples and introduction -- Akaike's information criterion -- The Bayesian information criterion -- A comparison of some selection methods -- Bigger is not always better -- The focussed information criterion -- Frequentist and Bayesian model averaging -- Lack-of-fit and goodness-of-fit tests -- Model selection and averaging schemes in action 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 Mathematisches Modell Mathematical models / Research Mathematical statistics / Research Bayesian statistical decision theory Modellwahl (DE-588)4304786-5 gnd rswk-swf Statistisches Modell (DE-588)4121722-6 gnd rswk-swf Mathematisches Modell (DE-588)4114528-8 gnd rswk-swf Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd rswk-swf Mathematisches Modell (DE-588)4114528-8 s Bayes-Entscheidungstheorie (DE-588)4144220-9 s 1\p DE-604 Statistisches Modell (DE-588)4121722-6 s Modellwahl (DE-588)4304786-5 s 2\p DE-604 Hjort, Nils Lid 1953- Sonstige (DE-588)137124562 oth Erscheint auch als Druckausgabe 978-0-521-85225-8 https://doi.org/10.1017/CBO9780511790485 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Claeskens, Gerda 1973- Model selection and model averaging Model selection : data examples and introduction -- Akaike's information criterion -- The Bayesian information criterion -- A comparison of some selection methods -- Bigger is not always better -- The focussed information criterion -- Frequentist and Bayesian model averaging -- Lack-of-fit and goodness-of-fit tests -- Model selection and averaging schemes in action Mathematisches Modell Mathematical models / Research Mathematical statistics / Research Bayesian statistical decision theory Modellwahl (DE-588)4304786-5 gnd Statistisches Modell (DE-588)4121722-6 gnd Mathematisches Modell (DE-588)4114528-8 gnd Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd |
| subject_GND | (DE-588)4304786-5 (DE-588)4121722-6 (DE-588)4114528-8 (DE-588)4144220-9 |
| title | Model selection and model averaging |
| title_alt | Model Selection & Model Averaging |
| title_auth | Model selection and model averaging |
| title_exact_search | Model selection and model averaging |
| title_full | Model selection and model averaging Gerda Claeskens, Nils Lid Hjort |
| title_fullStr | Model selection and model averaging Gerda Claeskens, Nils Lid Hjort |
| title_full_unstemmed | Model selection and model averaging Gerda Claeskens, Nils Lid Hjort |
| title_short | Model selection and model averaging |
| title_sort | model selection and model averaging |
| topic | Mathematisches Modell Mathematical models / Research Mathematical statistics / Research Bayesian statistical decision theory Modellwahl (DE-588)4304786-5 gnd Statistisches Modell (DE-588)4121722-6 gnd Mathematisches Modell (DE-588)4114528-8 gnd Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd |
| topic_facet | Mathematisches Modell Mathematical models / Research Mathematical statistics / Research Bayesian statistical decision theory Modellwahl Statistisches Modell Bayes-Entscheidungstheorie |
| url | https://doi.org/10.1017/CBO9780511790485 |
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