000			03932nam a22004815i 4500
001			978-0-387-76721-5
003			DE-He213
005			20161121231209.0
007			cr nn 008mamaa
008			100317s2008 xxu| s |||| 0|eng d
020			_a9780387767215 _9978-0-387-76721-5
024	7		_a10.1007/978-0-387-76721-5 _2doi
050		4	_aQA273.A1-274.9
050		4	_aQA274-274.9
072		7	_aPBT _2bicssc
072		7	_aPBWL _2bicssc
072		7	_aMAT029000 _2bisacsh
082	0	4	_a519.2 _223
245	1	0	_aRandom Effect and Latent Variable Model Selection _h[electronic resource] / _cedited by David B. Dunson.
264		1	_aNew York, NY : _bSpringer New York : _bImprint: Springer, _c2008.
300			_aX, 170 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aLecture Notes in Statistics, _x0930-0325 ; _v192
505	0		_aRandom Effects Models -- Likelihood Ratio Testing for Zero Variance Components in Linear Mixed Models -- Variance Component Testing in Generalized Linear Mixed Models for Longitudinal/Clustered Data and other Related Topics -- Bayesian Model Uncertainty in Mixed Effects Models -- Bayesian Variable Selection in Generalized Linear Mixed Models -- Factor Analysis and Structural Equations Models -- A Unified Approach to Two-Level Structural Equation Models and Linear Mixed Effects Models -- Bayesian Model Comparison of Structural Equation Models -- Bayesian Model Selection in Factor Analytic Models.
520			_aRandom effects and latent variable models are broadly used in analyses of multivariate data. These models can accommodate high dimensional data having a variety of measurement scales. Methods for model selection and comparison are needed in conducting hypothesis tests and in building sparse predictive models. However, classical methods for model comparison are not well justified in such settings. This book presents state of the art methods for accommodating model uncertainty in random effects and latent variable models. It will appeal to students, applied data analysts, and experienced researchers. The chapters are based on the contributors’ research, with mathematical details minimized using applications-motivated descriptions. The first part of the book focuses on frequentist likelihood ratio and score tests for zero variance components. Contributors include Xihong Lin, Daowen Zhang and Ciprian Crainiceanu. The second part focuses on Bayesian methods for random effects selection in linear mixed effects and generalized linear mixed models. Contributors include David Dunson and collaborators Bo Cai and Saki Kinney. The final part focuses on structural equation models, with Peter Bentler and Jiajuan Liang presenting a frequentist approach, Sik-Yum Lee and Xin-Yuan Song presenting a Bayesian approach based on path sampling, and Joyee Ghosh and David Dunson proposing a method for default prior specification and efficient posterior computation. David Dunson is Professor in the Department of Statistical Science at Duke University. He is an international authority on Bayesian methods for correlated data, a fellow of the American Statistical Association, and winner of the David Byar and Mortimer Spiegelman Awards.
650		0	_aMathematics.
650		0	_aProbabilities.
650		0	_aStatistics.
650	1	4	_aMathematics.
650	2	4	_aProbability Theory and Stochastic Processes.
650	2	4	_aStatistical Theory and Methods.
700	1		_aDunson, David B. _eeditor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387767208
830		0	_aLecture Notes in Statistics, _x0930-0325 ; _v192
856	4	0	_uhttp://dx.doi.org/10.1007/978-0-387-76721-5
912			_aZDB-2-SMA
950			_aMathematics and Statistics (Springer-11649)
999			_c509987 _d509987