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020 _a9780387713939
_99780387713939
024 7 _a10.1007/9780387713939
_2doi
035 _avtls000332144
039 9 _a201509030217
_bVLOAD
_c201404122036
_dVLOAD
_c201404091805
_dVLOAD
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040 _aMX-SnUAN
_bspa
_cMX-SnUAN
_erda
050 4 _aQA276-280
100 1 _aSong, Peter X.-K.
_eautor
_9304396
245 1 0 _aCorrelated Data Analysis: Modeling, Analytics, and Applications /
_cby Peter X.-K. Song.
264 1 _aNew York, NY :
_bSpringer New York,
_c2007.
300 _axv, 346 páginas
_brecurso en línea.
336 _atexto
_btxt
_2rdacontent
337 _acomputadora
_bc
_2rdamedia
338 _arecurso en línea
_bcr
_2rdacarrier
347 _aarchivo de texto
_bPDF
_2rda
490 0 _aSpringer Series in Statistics,
_x0172-7397
500 _aSpringer eBooks
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).
590 _aPara consulta fuera de la UANL se requiere clave de acceso remoto.
710 2 _aSpringerLink (Servicio en línea)
_9299170
776 0 8 _iEdición impresa:
_z9780387713922
856 4 0 _uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-0-387-71393-9
_zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999 _c279845
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