Antedependence Models for Longitudinal Data

Dale L. Zimmerman is a professor in the Department of Statistics and Actuarial Science at the University of Iowa.Vicente A. Núnez-Antón is a professor in the Department of Econometrics and Statistics at The University of the Basque Country.

• Introduction. Unstructured Antedependence Models. Structured Antedependence Models. Informal Model Identification. Likelihood-Based Estimation. Testing Hypotheses on the Covariance Structure. Testing Hypotheses on the Mean Structure. Case Studies. Further Topics and Extensions. Appendices. References. Index.

"The book is well written and clearly arranged and can be recommended to statisticians with interest in longitudinal data."—P.G. Hackl, Statistical Papers (2013) 54"When we fit models to describe a data, we try different models to determine a model that would describe the data better than the other models. This book provides a class of antedependence models to try. The authors made a valuable contribution to our profession by providing in this book some possible models along with the inference procedures for us to try to describe a longitudinal data better."—Subir Ghosh, Technometrics, May 2012"The motivation for this work was the authors’ view that such covariance structures have a much greater role to play in the analysis of longitudinal data than is commonly realized and that for ‘antedependence models to realize their full potential … this body of work needs to be brought together in one place…’. They have managed to achieve this aim both thoroughly and effectively. … this monograph provides a thorough treatment of the antedependence covariance structure in all its various forms and clearly illustrates its use in a variety of longitudinal settings."—Australian & New Zealand Journal of Statistics, 53(1), 2011"This book is the first one dedicated entirely to antedependece models … The material presented is well organized. … 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 documenting more in the near future. This is very important for applications, since there is little software developed for these models, and applied researchers will be very satisfied with the availability of R functions to fit antedependence models to their data. … the book is very useful as a supplementary material in a course on longitudinal data analysis, or it could be the basis of 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."—Raúl E. Macchiavelli, SORT, Vol. 34 (1), 2010"The main problems which need to be addressed in the context of longitudinal data analysis are mean and covariance specification. This text offers a fresh point of view to both of these issues. The textbook presents a detailed investigation of the class of antedependence models for modelling normally distributed longitudinal data. … I enjoyed reading the book and I believe that this work is a useful addition to the texts on longitudinal data analysis. … The main concepts and ideas are presented smoothly and without any ambiguities. This book is suitable as a supplement text for an advanced graduate course or for self-study by doctoral students or researchers in statistics and biostatistics. … an excellent addition to the literature and I recommend it to any professional statistician."—Journal of Time Series Analysis, 2010