Correlated Data Analysis: Modeling, Analytics, and Applications

ISBN-13: 9781441924407 / Angielski / Miękka / 2010 / 352 str.

Peter X. -K Song

Opis

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 application

Recenzje

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)

Spis treści

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.

O autorze

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.

Blurb

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).