Correlated Data Analysis: Modeling, Analytics, and Applications

Peter X. -K. Song

ISBN 10: 144192440X / ISBN 13: 9781441924407

Published by Springer New York, 2010

About this Item

Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 modelsA rigorous presentation of the theory of inference functions prior to the i.

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Bibliographic Details

• Title: Correlated Data Analysis: Modeling, Analytics, and Applications
• Publisher: Springer New York
• Publication Date: 2010
• Language: English
• ISBN 10: 144192440X
• ISBN 13: 9781441924407
• Binding: Soft cover
• Condition: New

About this title

Synopsis

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.

From the Back Cover

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).