The Springer Series in Statistics includes a collection of books on statistical methods and models. Notable titles include "Statistical Models Based on Counting Processes," "Statistical Decision Theory and Bayesian Analysis," "Prediction Theory for Finite Populations," "Modern Multidimensional Scaling," "Time Series: Theory and Methods," "Monte Carlo Methods in Bayesian Computation," and many others. These books cover a wide range of topics in statistics, including nonparametric curve estimation, multivariate statistical modeling, and statistical design for intercropping experiments. The book "Linear Mixed Models for Longitudinal Data" by Geert Verbeke and Geert Molenberghs provides an introduction to linear mixed models, which are used to analyze longitudinal data. The authors emphasize the practical application of these models, particularly in biomedical research. The book covers topics such as model building, estimation, inference, and the use of software like SAS. It also includes examples from various studies, such as the Rat Data, Prostate Data, and Hearing Data, to illustrate the application of linear mixed models. The authors acknowledge the support of various individuals and organizations in the development of the book. They also thank the publishers and their families for their support during the writing process. The book is intended for a wide audience, including applied statisticians and biomedical researchers, particularly in the pharmaceutical industry, medical and public health research organizations, and academic departments. It emphasizes practical issues rather than mathematical rigor, providing guidance on the implementation of linear mixed models in real-world scenarios. The book also includes a detailed discussion of the SAS procedure MIXED and other software tools for fitting mixed models. The authors have included a variety of examples and case studies to demonstrate the application of linear mixed models in different contexts. The book is structured to reflect both the authors' research and their experience in teaching applied longitudinal modeling courses. It is a comprehensive resource for those interested in the analysis of longitudinal data using linear mixed models.The Springer Series in Statistics includes a collection of books on statistical methods and models. Notable titles include "Statistical Models Based on Counting Processes," "Statistical Decision Theory and Bayesian Analysis," "Prediction Theory for Finite Populations," "Modern Multidimensional Scaling," "Time Series: Theory and Methods," "Monte Carlo Methods in Bayesian Computation," and many others. These books cover a wide range of topics in statistics, including nonparametric curve estimation, multivariate statistical modeling, and statistical design for intercropping experiments. The book "Linear Mixed Models for Longitudinal Data" by Geert Verbeke and Geert Molenberghs provides an introduction to linear mixed models, which are used to analyze longitudinal data. The authors emphasize the practical application of these models, particularly in biomedical research. The book covers topics such as model building, estimation, inference, and the use of software like SAS. It also includes examples from various studies, such as the Rat Data, Prostate Data, and Hearing Data, to illustrate the application of linear mixed models. The authors acknowledge the support of various individuals and organizations in the development of the book. They also thank the publishers and their families for their support during the writing process. The book is intended for a wide audience, including applied statisticians and biomedical researchers, particularly in the pharmaceutical industry, medical and public health research organizations, and academic departments. It emphasizes practical issues rather than mathematical rigor, providing guidance on the implementation of linear mixed models in real-world scenarios. The book also includes a detailed discussion of the SAS procedure MIXED and other software tools for fitting mixed models. The authors have included a variety of examples and case studies to demonstrate the application of linear mixed models in different contexts. The book is structured to reflect both the authors' research and their experience in teaching applied longitudinal modeling courses. It is a comprehensive resource for those interested in the analysis of longitudinal data using linear mixed models.