The book "Linear and Nonlinear Models for the Analysis of Repeated Measurements" by Edward F. Vonesh and Vernon M. Chinchilli provides a comprehensive overview of methods for analyzing repeated measurements data. The authors aim to cover both theoretical and practical aspects, making it suitable for statisticians, graduate students, and research scientists involved in the design and analysis of repeated measurements experiments. The book emphasizes applications in biomedical and life sciences but can be applied to other disciplines such as education, psychology, and social sciences. Key topics include: - **Matrix Algebra and Multivariate Distribution Theory**: Basic results in matrix algebra, multivariate normal distribution, quadratic forms, and asymptotic theory. - **Multivariate Analysis of Variance (MANOVA) Model**: Estimation, hypothesis testing, and diagnostics. - **ANOVA and MANOVA for Repeated Measures**: Single-sample and multi-factor designs, covariance models, and sample size determination. - **Crossover Designs**: Classical techniques and recent developments using mixed-effects models. - **Linear Mixed-Effects Models**: Generalized multivariate analysis of variance (GMANOVA) model, random-coefficient growth curve models, and linear mixed-effects models. - **Nonlinear Regression Models**: Population-averaged and subject-specific models, generalized nonlinear mixed-effects models, and estimation methods. - **Software**: The book includes computer programs in SAS for analyzing repeated measurements data, including MIXNLIN, a software program for nonlinear models. The authors also discuss the strengths and weaknesses of repeated measurements data, such as efficient evaluation of change within and between individuals and the need to handle heterogeneity and missing data. The book is designed to be a valuable resource for researchers and practitioners in various fields, providing a blend of theory, methodology, and practical applications.The book "Linear and Nonlinear Models for the Analysis of Repeated Measurements" by Edward F. Vonesh and Vernon M. Chinchilli provides a comprehensive overview of methods for analyzing repeated measurements data. The authors aim to cover both theoretical and practical aspects, making it suitable for statisticians, graduate students, and research scientists involved in the design and analysis of repeated measurements experiments. The book emphasizes applications in biomedical and life sciences but can be applied to other disciplines such as education, psychology, and social sciences. Key topics include: - **Matrix Algebra and Multivariate Distribution Theory**: Basic results in matrix algebra, multivariate normal distribution, quadratic forms, and asymptotic theory. - **Multivariate Analysis of Variance (MANOVA) Model**: Estimation, hypothesis testing, and diagnostics. - **ANOVA and MANOVA for Repeated Measures**: Single-sample and multi-factor designs, covariance models, and sample size determination. - **Crossover Designs**: Classical techniques and recent developments using mixed-effects models. - **Linear Mixed-Effects Models**: Generalized multivariate analysis of variance (GMANOVA) model, random-coefficient growth curve models, and linear mixed-effects models. - **Nonlinear Regression Models**: Population-averaged and subject-specific models, generalized nonlinear mixed-effects models, and estimation methods. - **Software**: The book includes computer programs in SAS for analyzing repeated measurements data, including MIXNLIN, a software program for nonlinear models. The authors also discuss the strengths and weaknesses of repeated measurements data, such as efficient evaluation of change within and between individuals and the need to handle heterogeneity and missing data. The book is designed to be a valuable resource for researchers and practitioners in various fields, providing a blend of theory, methodology, and practical applications.