” . . . I think, this book should be a must for any scientist dealing with the problem of analyzing mixed data. And, I would also like to thank the editors, A. R. de Leon and K. C. Chough, for such a nice compilation of very interesting and recent works for us.”
–Abhik Ghosh, International Society for Clinical Biostatistics

“…the book is well written. The editors have done a wonderful job of selecting the mix of topics to include in the volume, thereby providing the reader with the flavor of the diversity of areas where mixed data analysis is now standard practice. The technical level of the book will make it appealing to a wide audience–from methodologically oriented researchers, such as graduate students and researchers in statistics and biostatistics, to those interested in subject matter areas, such as medicine, genetics, and social sciences.”
—Journal of the American Statistical Association, June 2014

“This is a well-written book with broad coverage on various topics … Each chapter provides carefully selected examples and cases to show the application of presented methods and pointed out possible future research directions. This book has a wide coverage on recent methodological developments for mixed data analysis involving GLMM, copula models, Bayesian methods, and latent variable modeling as well as on the applications of mixed data analysis in various areas, including biology, epidemiology, econometrics, health policy, and social science. In summary, this is an outstanding research book for researchers interested in analysis of mixed data.”
–Zhigang Li, Journal of Biopharmaceutical Statistics, 2014

“As far as I know, this is the first work in this area, which provides an excellent overview about statistical models, estimation, and applications. … The most impressive feature of the book is its broad scope; it covers most of the topics that are common for mixed data analysis. … This book includes cross-references between chapters with a combined index and uses unified notations, table formats, and terminologies across chapters. All these features enable readers to easily access the various topics of mixed data analysis. … this book is well written and well organized. It gives an excellent overview of mixed data analysis both in terms of methods and applications. In addition to the statistics area, this book would be a good reference for researchers and professionals from different areas such as developmental toxicology, economics, medicine and health, marketing, and genetics.”
—Biometrics, June 2014

Alexander R. de Leon is Associate Professor in the Department of Mathematics and Statistics at the University of Calgary. Originally from the Philippines, he obtained his BSc and MSc, both in Statistics, from the School of Statistics of the University of the Philippines. After a research studentship at Tokyo University of Science, he completed his PhD in Statistics in 2002 at the University of Alberta. His research interests include methods for analyzing correlated data, multivariate models and distances for mixed discrete and continuous outcomes, pseudo- and composite likelihood methods, copula modeling, assessment of diagnostic tests, statistical quality control, and statistical problems in medicine, particularly in ophthalmology. Alex can be reached at adeleon@ucalgary.ca.

Keumhee Carriere Chough is Professor of Statistics in the Department of Mathematical and Statistical Sciences at the University of Alberta. After completing her BSc in Agriculture from Seoul National University, in Seoul, Korea, she earned her MSc from the University of Manitoba, and her PhD in Statistics from the University of Wisconsin-Madison in 1989. Since 1996, she has been with the Department of Mathematical and Statistical Sciences, University of Alberta, after stints as Assistant Professor at the University of Iowa (1990–1992) and University of Manitoba (1992–1996). She was also the Director of the Statistics Consulting Center at the University of Iowa (1990–1992). Her research interests include design and analysis for repeated measures data, missing data methods, high dimensional data analysis methods, multivariate methods, designs for clinical trials, item response data, variable selection methods, and survival analysis. As well, she specializes in such biostatistical methods as small area variation analysis techniques with applications to health care utilization.