Growth Curve Models and Statistical Diagnostics (Springer Series in Statistics)

Growth-curve models are generalized multivariate analysis-of-variance models. These models are especially useful for investigating growth problems on short times in economics, biology, medical research, and epidemiology. This book systematically introduces the theory of the GCM with particular emphasis on their multivariate statistical diagnostics, which are based mainly on recent developments made by the authors and their collaborators. The authors provide complete proofs of theorems as well as practical data sets and MATLAB code.

From the reviews:

"The book is well written and contains a goodly number of real-data applications." ISI

Short Book Reviews, Vol.23/1, April 2003

"This book offers an extensive view of Growth Curve Models and a wide range of issues related with statistical diagnosis. ... Each chapter ends with some bibliographical notes that inform the reader about historical sources as well as about recent developments. The bibliographic list is impressive! ... the information given about Growth Curve Models and Statistical Diagnosis is excellent. The book is written very rigorously and precisely and I strongly recommend it for statisticians or for applied scientists with some mathematical and statistical background." (Prof. C. García-Olaverri, Kwantitatieve Methoden, Vol. 72B5, 2003)

"The authors have written a basic book on a well developed and important field in multivariate statistical analysis. It will undoubtedly serve as a reference in this field." (Arjun K. Gupta, Zentralblatt MATH, Vol. 1024, 2003)

"This book presents methods for analyzing repeated measures and longitudinal data using the growth curve models (GCMs), with specific focus on the generalized multivariate analysis of variance (GMANOVA) model. ... For researchers, the book’s main strength is its level of detail. ... Theoreticians in multivariate analysis will find this book to be a good reference for this particular GCM and multivariate regression diagnosis." (Andrew M. Kuhn, Technometrics, Vol. 45 (3), 2003)

"Models are discussed for data variously described as growth curves, longitudinal data, or multilevel data. The text supplements the growing number of references on techniques for longitudinal data by focusing on diagnostics for outliers and influential observations. ... the book is well written and does contain a goodly number of real-data applications." (J. O. Ramsey, Short Book Reviews, Vol. 23 (1), 2003)