Given the importance of linear models in statistical theory and experimental research, a good understanding of their fundamental principles and theory is essential. Supported by a large number of examples, Linear Model Methodology provides a strong foundation in the theory of linear models and explores the latest developments in data analysis.After

Andre I. Khuri is a Professor Emeritus in the Department of Statistics at the University of Florida in Gainesville.

Supported by a large number of examples, this book provides a strong foundation in the theory of linear models and explores the latest developments in data analysis. It encompasses a wide variety of topics in linear models that incorporate the classical approach and more recent trends and modeling techniques. The author emphasizes the central role matrices have played in the modern development of linear models and presents a unified approach to modeling discrete and continuous response data. He also covers both balanced mixed-effects models and unbalanced linear models so that readers have a full understanding of how to analyze data under different modeling situations.

Linear Models: Some Historical Perspectives. Basic Elements of Linear Algebra. Basic Concepts in Matrix Algebra. The Multivariate Normal Distribution. Quadratic Forms in Normal Variables. Full-Rank Linear Models. Less-Than-Full-Rank Linear Models. Balanced Linear Models. The Adequacy of Satterthwaite's Approximation. Unbalanced Fixed-Effects Models. Unbalanced Random and Mixed Models. Additional Topics in Linear Models. Generalized Linear Models. Bibliography. Index.