© 2007

Correlated Data Analysis: Modeling, Analytics, and Applications


Part of the Springer Series in Statistics book series (SSS)

Table of contents

  1. Front Matter
    Pages I-XV
  2. Peter X.-K. Song
    Pages 1-21
  3. Peter X.-K. Song
    Pages 23-53
  4. Peter X.-K. Song
    Pages 55-71
  5. Peter X.-K. Song
    Pages 73-85
  6. Peter X.-K. Song
    Pages 87-120
  7. Peter X.-K. Song
    Pages 121-155
  8. Peter X.-K. Song
    Pages 157-194
  9. Peter X.-K. Song
    Pages 195-215
  10. Peter X.-K. Song
    Pages 217-226
  11. Peter X.-K. Song
    Pages 227-237
  12. Peter X.-K. Song
    Pages 291-328
  13. Back Matter
    Pages 329-346

About this book


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).


Generalized linear model Likelihood Regression analysis Sage Time series copula data analysis dispersion model estimating function longitudinal data state space model

Authors and affiliations

  1. 1.Department of Statistics and Actuarial ScienceUniversity of Waterloo200 University Avenue WestWaterlooCanada N2L 3G1

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From the reviews:

"The book presents recent developments in the field of correlated data analysis. Its aim is to give a systematic account of regression models and their application to the modelling and analysis of correlated data. … Various real-world data examples, numerical illustrations and software usage tips are presented throughout the book, making it suitable for graduate courses on correlated data analysis. … also serve as a reference for those that need theoretical explanations and a deeper understanding of the theory that underlies the related analyses." (Christina Diakaki, Zentralblatt MATH, Vol. 1132 (10), 2008)

"This is an ambitious book that covers an enormous amount of material in a relatively small number of pages. … would be a good addition to the library of a statistician interested in both the theoretical and applied aspects of correlated data analysis. … it would be a good choice for a graduate-level course focusing on the theoretical aspects of longitudinal and discrete time series data analysis. It also might serve as a good reference book for a more applied course on this subject." (Paul S. Albert, Journal of the American Statistical Association, Vol. 103 (484), December, 2008)

"This book is a highly recommended text for those armed with a strong computational background and ambitious enough to attack real world problems of high dimension, unknown complexity, and at most hazy knowledge of the causalities. Such problems abound. i:.g., in medicine, biology, meteorology, and climate change.… The results as presented are impressive. It is amazing what can be done if one uses available software. e.g., from SAS at several instances or of related software sources, e.g., WINBUGS or "R." Anyone doing similar empirical work should read this book. (Götz Ube, AStA - Advances in Statistical Analysis. DOI 10.1007/s10182-008-9)

"This book focuses on correlated data analysis and is divided into three main parts. … The structure of the content is quite helpful. There are clearly laid out SAS codes that are useful for researchers. … the book is easy to read and comprehend and it can serve as a very good guide to correlated data analysis and a useful tool in the hands of researchers and graduate students. The book is well suited to professionals working in the medical, biomedical, and econometric fields." (Filia Vonta, Mathematical Reviews, Issue 2009 e)

“The book provides several advanced mathematical tools for correlated data analysis that are useful for research and instructional purposes . In my opinion, the title Correlated Data Analysis: Modeling, Analytics, and Applications reflects the book’s content perfectly. The book is very pleasant to read, and I have no doubt that Technometrics readers will enjoy reading it. … The book is intended for statisticians or biostatistician researchers whose research interests involve theory and approaches of correlated data analysis. It addresses advanced theoretical problems arising in analysis of correlated data sets and several mathematical results underlying generalized estimating equations and quadratic inference function. The mathematical results are derived with a balance between details and elegant technical tools The book also addresses several practical problems arising in the analysis of correlated data sets and describes some real data sets that are made available to the reader. It also can serve as a good reference for a graduate students in the areas of statistics, biostatistics, or other areas where correlated data analysis is needed. … In general, this book is very well written, well organized, and clear. The derivations of mathematical results are given with a perfect blend of simplicity, rigor, technical tools. And details. …I think that reader will thoroughly enjoy this book…” (Technometrics, May 2010, Vol. 52, No. 2)