000			02772nam a22003978i 4500
001			CR9780511790485
003			UkCbUP
005			20200124160218.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			100611s2008||||enk o ||1 0|eng|d
020			_a9780511790485 (ebook)
020			_z9780521852258 (hardback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aQA276.18 _b.C53 2008
082	0	0	_a519.5 _222
100	1		_aClaeskens, Gerda, _d1973- _eauthor.
245	1	0	_aModel selection and model averaging / _cGerda Claeskens, Nils Lid Hjort.
246	3		_aModel Selection & Model Averaging
264		1	_aCambridge : _bCambridge University Press, _c2008.
300			_a1 online resource (xvii, 312 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	1		_aCambridge series on statistical and probabilistic mathematics ; _v27
500			_aTitle from publisher's bibliographic system (viewed on 05 Oct 2015).
505	0		_aModel 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.
520			_aGiven 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.
650		0	_aMathematical models _xResearch.
650		0	_aMathematical statistics _xResearch.
650		0	_aBayesian statistical decision theory.
700	1		_aHjort, Nils Lid, _eauthor.
776	0	8	_iPrint version: _z9780521852258
830		0	_aCambridge series on statistical and probabilistic mathematics ; _v27.
856	4	0	_uhttps://doi.org/10.1017/CBO9780511790485
999			_c516494 _d516492