This 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.
General
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