Correlated Data Analysis: Modeling, Analytics, and Applications

1. Front Matter

   Pages I-XV

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2. Introduction and Examples

   • Peter X.-K. Song

   Pages 1-21

3. Dispersion Models

   • Peter X.-K. Song

   Pages 23-53

4. Inference Functions

   • Peter X.-K. Song

   Pages 55-71

5. Modeling Correlated Data

   • Peter X.-K. Song

   Pages 73-85

6. Marginal Generalized Linear Models

   • Peter X.-K. Song

   Pages 87-120

7. Vector Generalized Linear Models

   • Peter X.-K. Song

   Pages 121-155

8. Mixed-Effects Models: Likelihood-Based Inference

   • Peter X.-K. Song

   Pages 157-194

9. Mixed-Effects Models: Bayesian Inference

   • Peter X.-K. Song

   Pages 195-215

10. Linear Predictors

    • Peter X.-K. Song

    Pages 217-226

11. Generalized State Space Models

    • Peter X.-K. Song

    Pages 227-237

12. Generalized State Space Models for Longitudinal Binomial Data

    • Peter X.-K. Song

    Pages 239-260

13. Generalized State Space Models for Longitudinal Count Data

    • Peter X.-K. Song

    Pages 261-290

14. Missing Data in Longitudinal Studies

    • Peter X.-K. Song

    Pages 291-328

15. Back Matter

    Pages 329-346

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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.

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

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)

• Department of Statistics and Actuarial Science, University of Waterloo, 200 University Avenue West, Waterloo, Canada N2L 3G1

  Peter X.-K. Song

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