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100 1 Claeskens, Gerda,|d1973-|0https://id.loc.gov/authorities/
names/n2008010287|eauthor.
245 10 Model selection and model averaging /|cGerda Claeskens,
K.U. Leuven, Nils Lid Hjort, University of Oslo.
264 1 Cambridge ;|aNew York :|bCambridge University Press,
|c2008.
264 4 |c©2008
300 1 online resource (xvii, 312 pages) :|billustrations.
336 text|btxt|2rdacontent
337 computer|bc|2rdamedia
338 online resource|bcr|2rdacarrier
340 |gpolychrome|2rdacc
347 text file|2rdaft
490 1 Cambridge series in statistical and probabilistic
mathematics
504 Includes bibliographical references (pages 293-305) and
indexes.
505 0 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 -- Further topics.
520 1 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 suitable model is central to all statistical
work with data. Selecting the variables for use in a
regression model is one important example. The past two
decades have seen rapid advances both in our ability to
fit models and in the theoretical understanding of model
selection needed to harness this ability, yet this book is
the first to provide a synthesis of research from this
active field, and it contains much material previously
difficult or impossible to find. In addition, it gives
practical advice to the researcher confronted with
conflicting results." "Model choice criteria are explained,
discussed and compared, including Akaike's information
criterion AIC, the Bayesian information criterion BIC and
the focused information criterion FIC. Importantly, the
uncertainties involved with model selection are addressed,
with discussions of frequentist and Bayesian methods.
Finally, model averaging schemes, which combine the
strengths of several candidate models, are presented."--
Jacket.
588 0 Print version record.
590 eBooks on EBSCOhost|bEBSCO eBook Subscription Academic
Collection - North America
650 0 Mathematical models|0https://id.loc.gov/authorities/
subjects/sh85082124|xResearch.|0https://id.loc.gov/
authorities/subjects/sh2002006576
650 0 Mathematical statistics|0https://id.loc.gov/authorities/
subjects/sh85082133|xResearch.|0https://id.loc.gov/
authorities/subjects/sh2002006576
650 0 Bayesian statistical decision theory.|0https://id.loc.gov/
authorities/subjects/sh85012506
650 7 Mathematical models|xResearch.|2fast|0https://
id.worldcat.org/fast/1012090
650 7 Mathematical models.|2fast|0https://id.worldcat.org/fast/
1012085
650 7 Mathematical statistics|xResearch.|2fast|0https://
id.worldcat.org/fast/1012144
650 7 Mathematical statistics.|2fast|0https://id.worldcat.org/
fast/1012127
650 7 Bayesian statistical decision theory.|2fast|0https://
id.worldcat.org/fast/829019
655 4 Electronic books.
700 1 Hjort, Nils Lid,|0https://id.loc.gov/authorities/names/
nb91406389|eauthor.
776 08 |iPrint version:|aClaeskens, Gerda, 1973-|tModel selection
and model averaging.|dCambridge ; New York : Cambridge
University Press, 2008|z9780521852258|z0521852250|w(DLC)
2008006507|w(OCoLC)199455609
830 0 Cambridge series on statistical and probabilistic
mathematics.|0https://id.loc.gov/authorities/names/
n96064948
856 40 |uhttps://rider.idm.oclc.org/login?url=http://
search.ebscohost.com/login.aspx?direct=true&scope=site&
db=nlebk&AN=244506|zOnline eBook via EBSCO. Access
restricted to current Rider University students, faculty,
and staff.
856 42 |3Instructions for reading/downloading the EBSCO version
of this eBook|uhttp://guides.rider.edu/ebooks/ebsco
901 MARCIVE 20231220
948 |d20190507|cEBSCO|tEBSCOebooksacademic NEW 4-5-19 7552
|lridw
994 92|bRID
