Correlated Data Analysis: Modeling, Analytics, and Applications

This book covers recent developments in correlated data analysis, using the class of dispersion models as marginal components in the formulation of joint models for correlated data. Much new material is covered here that you won't find elsewhere.

Aimed at graduate students and researchers, this book deals with recent developments in correlated data analysis. It uses the class of dispersion models as marginal components in the formulation of joint models for correlated data. This enables the book to cover a broader range of data types than the traditional generalized linear models, such as correlated directional data and correlated compositional data. The reader is provided with a systematic treatment for the topic of estimating functions, and both generalized estimating equations (GEE) and quadratic inference functions (QIF) are studied as special cases. In addition to the discussions on marginal models and mixed-effects models, this book covers new topics on joint regression analysis based on Gaussian copulas and state space models for longitudinal data from long time series. Various real-world data examples, numerical illustrations and software usage tips are included too. Applied statisticians and data analysts in many subject-matter fields will find this text essential.

New topics are featured that have not been discussed in other books: a unified framework of model for clustered, longitudinal, or vector outcomes based on dispersion models A rigorous presentation of the theory of inference functions prior to the introduction to the marginal models The means of quadratic inference function (QIF) The theory of vector generalized linear models...and more! Includes supplementary material: sn.pub/extras

Klappentext

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.

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).

Inhalt
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.