Obra ressenyada: Dale ZIMMERMAN and Vicente NÚÑEZ ANTÓN, Antedependence Models for Longitudinal Data. Chapman & Hall/CRC, 2010

Machiavelli, Raúl E.

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Antedependence Models for Longitudinal DataDale Zimmerman and Vicente Nu´n˜ez Anto´nChapman & Hall / CRC, 2010, 270 ppLongitudinal data analysis has become an extremely important area of statistics, andthis is reflected both in the diversity of the real life applications and in the depth of themethods developed for this type of data. This book is the first one dedicated entirely toantedependece models, which are a very rich family of conditional independence modelsfor serially correlated data. Following the ideas of Diggle et al. (2002), the inferenceproblem in longitudinal data is approached first by selecting an appropriate covariancestructure (which in most applications is considered as a set of nuisance parameters) andthen making inferences on the mean parameters (which generally are the ones of primaryinterest).The material presented is well organized. The first chapter presents motivational ex-amples and the concept of antedependence models. The second chapter characterizesmore formally the unstructured antedependence models, and shows equivalent ways ofdefining them. The structured models are presented and discussed in chapter 3, togetherwith related models for serially correlated data. Chapter 4 presents exploratory tech-niques to identify these models, and shows examples of both numerical and graphicaldiagnostic tools. Formal likelihood-based inference is presented in chapters 5-7. Chap-ter 5 introduces estimation techniques for different mean models, both for completeand incomplete data (including several patterns of missing data common in longitudinaldata). Chapter 6 presents hypothesis tests and related tools (such as penalized likelihoodcriteria) for the covariance structure, and chapter 7 approaches the problem of testinghypotheses on the mean parameters. The use of most of the techniques presented isillustrated in the four data sets presented in chapter 1, and chapter 8 integrates theseexamples into case studies for each of the data sets. Chapter 9 presents some additionaltopics and extensions. Several matrix results and more technical proofs are presented asappendices.Several relevant R functions are available for download from the first author’s webpage(http://www.stat.uiowa.edu/∼dzimmer), and the authors are making available and doc-umenting more in the near future. This is very important for applications, since there islittle software developed for these models, and applied researchers will be very satisfiedwith the availability of R functions to fit antedependence models to their data.The book is geared towards statisticians (both theoretical and applied) and other re-searchers in application areas (medicine, epidemiology, animal science, forestry, ecol-ogy) with experience in linear models, multivariate analysis, and/or longitudinal dataanalysis. For graduate students in statistics or biostatistics, the book is very useful as asupplementary material in a course on longitudinal data analysis, or it could be the basisof a special topics course on antedependence models.In summary, this book is a welcome addition to the bibliography of longitudinal data,combining rigorous statistical presentation with interesting real life examples.ReferencesDiggle, P. J., Heagerty, P. J., Liang, K. Y. and Zeger, S. L. (2002). Analysis of Longitudinal Data. 2nd ed.,New York: Oxford University Press.Rau´l E. MacchiavelliUniversity of Puerto Rico Mayagu¨ez

Antedependence Models for Longitudinal Data. Dale Zimmerman and Vicente Núñez Antón. Chapman & Hall/CRC, 2010, 270 pages

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Antedependence Models for Longitudinal Data. Dale Zimmerman and Vicente Núñez Antón. Chapman & Hall/CRC, 2010, 270 pages

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