Correlated Data Analysis: Modeling, Analytics, and Applications

Peter X. -K. Song (Autor)

Buch | Softcover

352 Seiten

2010 | Softcover reprint of hardcover 1st ed. 2007
Springer-Verlag New York Inc.
978-1-4419-2440-7 (ISBN)

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Thisbook,likemanyotherbooks,wasdeliveredundertremendousinspiration and encouragement from my teachers, research collaborators, and students. My interest in longitudinal data analysis began with a short course taught jointly by K. Y. Liang and S. L. Zeger at the Statistical Society of Canada Conference in Acadia University, Nova Scotia, in the spring of 1993. At that time, I was a ?rst-year PhD student in the Department of Statistics at the University of British Columbia, and was eagerly seeking potential topics for my PhD dissertation. It was my curiosity (driven largely by my terrible c- fusion) with the generalized estimating equations (GEEs) introduced in the short course that attracted me to the ?eld of correlated data analysis. I hope that my experience in learning about it has enabled me to make this book an enjoyable intellectual journey for new researchers entering the ?eld. Thus, the book aims at graduate students and methodology researchers in stat- tics or biostatistics who are interested in learning the theory and methods of correlated data analysis. I have attempted to give a systematic account of regression models and their applications to the modeling and analysis of correlated data. Longitu- nal data, as an important type of correlated data, has been used as a main venue for motivation, methodological development, and illustration throu- out the book. Given the many applied books on longitudinal data analysis - ready available, this book is inclined more towards technical details regarding the underlying theory and methodology used in software-based applications.

and 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.

Erscheint lt. Verlag	29.11.2010
Reihe/Serie	Springer Series in Statistics
Zusatzinfo	XVI, 352 p.
Verlagsort	New York, NY
Sprache	englisch
Maße	155 x 235 mm
Themenwelt	Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik
ISBN-10	1-4419-2440-X / 144192440X
ISBN-13	978-1-4419-2440-7 / 9781441924407
Zustand	Neuware