000			02912nam a22004215i 4500
001			u372430
003			SIRSI
005			20160812084058.0
007			cr nn 008mamaa
008			110616s2011 xxu| s |||| 0|eng d
020			_a9781461404996 _9978-1-4614-0499-6
040			_cMX-MeUAM
050		4	_aQA276-280
082	0	4	_a519.5 _223
100	1		_aZiegler, Andreas. _eauthor.
245	1	0	_aGeneralized Estimating Equations _h[recurso electrónico] / _cby Andreas Ziegler.
264		1	_aNew York, NY : _bSpringer New York, _c2011.
300			_aXV, 144p. _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 ; _v204
505	0		_aThe linear exponential family -- The quadratic exponential family -- Generalized linear models -- Maximum likelihood method -- Quasi maximum likelihood method -- Pseudo maximum likelihood method based on the linear exponential family -- Quasi generalized pseudo maximum likelihood method based on the linear exponential family -- Algorithms for solving the generalized estimating equations and the relation to the jack-knife estimator of variance -- Pseudo maximum likelihood estimation based on the quadratic exponential family -- Generalized method of moment estimation.
520			_aGeneralized estimating equations have become increasingly popular in biometrical, econometrical, and psychometrical applications because they overcome the classical assumptions of statistics, i.e. independence and normality, which are too restrictive for many problems. Therefore, the main goal of this book is to give a systematic presentation of the original generalized estimating equations (GEE) and some of its further developments. Subsequently, the emphasis is put on the unification of various GEE approaches. This is done by the use of two different estimation techniques, the pseudo maximum likelihood (PML) method and the generalized method of moments (GMM). The author details the statistical foundation of the GEE approach using more general estimation techniques. The book could therefore be used as basis for a course to graduate students in statistics, biostatistics, or econometrics, and will be useful to practitioners in the same fields.
650		0	_aStatistics.
650		0	_aMathematical statistics.
650	1	4	_aStatistics.
650	2	4	_aStatistical Theory and Methods.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781461404989
830		0	_aLecture Notes in Statistics, _x0930-0325 ; _v204
856	4	0	_zLibro electrónico _uhttp://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-1-4614-0499-6
596			_a19
942			_cLIBRO_ELEC
999			_c200310 _d200310