Generalized Least Squares

Regression analysis has been one of the most widely used statistical tools for many years, and continues to be developed and applied to new applications. Generalized least squares estimation (GLSE) based on Gauss-Markov theory plays a key role in understanding theoretical and practical aspects of statistical inference in general linear regression models. GLSE can be applied to problems encountered in many disciplines, particularly statistics, econometrics, and biometrics. Provides a self-contained introduction to GLSE. Includes detailed coverage of the 'lower and upper bounds' approach, pioneered by the authors. Adopts a concise yet mathematically rigorous approach. Includes applications to statistics, econometrics, and biometrics. Contains exercises at the end of each chapter, enabling use as a course text or for self-study. Includes a comprehensive bibliography. Generalized Least Squares provides an accessible introduction to GLSE suitable for researchers and graduate students from statistics, econometrics, and biometrics. It provides an excellent source of reference, can be used as a course text, and will help to stimulate further research into this flourishing topic.