Correlated Data Analysis: Modeling, Analytics, and Applications



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Éditeur :

Springer


Collection :

Springer Series in Statistics

Paru le : 2007-06-30



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Description
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.
Pages
352 pages
Collection
Springer Series in Statistics
Parution
2007-06-30
Marque
Springer
EAN papier
9780387713922
EAN PDF
9780387713939

Informations sur l'ebook
Nombre pages copiables
3
Nombre pages imprimables
35
Taille du fichier
6625 Ko
Prix
105,49 €