000			03640nam a22004095i 4500
001			978-0-387-71393-9
003			DE-He213
005			20250710084012.0
007			cr nn 008mamaa
008			100301s2007 xxu| s |||| 0|eng d
020			_a9780387713939 _a99780387713939
024	7		_a10.1007/978-0-387-71393-9 _2doi
082	0	4	_a519.5 _223
100	1		_aSong, Peter X.-K. _eauthor.
245	1	0	_aCorrelated Data Analysis: Modeling, Analytics, and Applications _h[recurso electrónico] / _cby Peter X.-K. Song.
264		1	_aNew York, NY : _bSpringer New York, _c2007.
300			_aXV, 346 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSpringer Series in Statistics, _x0172-7397
505	0		_aand Examples -- Dispersion Models -- Inference Functions -- Modeling Correlated Data -- Marginal Generalized Linear Models -- Vector Generalized Linear Models -- Mixed-Effects Models: Likelihood-Based Inference -- Mixed-Effects Models: Bayesian Inference -- Linear Predictors -- Generalized State Space Models -- Generalized State Space Models for Longitudinal Binomial Data -- Generalized State Space Models for Longitudinal Count Data -- Missing Data in Longitudinal Studies.
520			_aThis book presents some recent developments in correlated data analysis. It utilizes the class of dispersion models as marginal components in the formulation of joint models for correlated data. This enables the book to handle a broader range of data types than those analyzed by traditional generalized linear models. One example is correlated angular data. This book provides a systematic treatment for the topic of estimating functions. Under this framework, both generalized estimating equations (GEE) and quadratic inference functions (QIF) are studied as special cases. In addition to marginal models and mixed-effects models, this book covers topics on joint regression analysis based on Gaussian copulas and generalized state space models for longitudinal data from long time series. Various real-world data examples, numerical illustrations and software usage tips are presented throughout the book. This book has evolved from lecture notes on longitudinal data analysis, and may be considered suitable as a textbook for a graduate course on correlated data analysis. This book is inclined more towards technical details regarding the underlying theory and methodology used in software-based applications. Therefore, the book will serve as a useful reference for those who want theoretical explanations to puzzles arising from data analyses or deeper understanding of underlying theory related to analyses. Peter Song is Professor of Statistics in the Department of Statistics and Actuarial Science at the University of Waterloo. Professor Song has published various papers on the theory and modeling of correlated data analysis. He has held a visiting position at the University of Michigan School of Public Health (Ann Arbor, Michigan).
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: _z9780387713922
830		0	_aSpringer Series in Statistics, _x0172-7397
856	4	0	_uhttp://dx.doi.org/10.1007/978-0-387-71393-9 _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-SMA
942			_2ddc _cER
999			_c58243 _d58243