Linear Models and Generalizations: Least Squares and Alternatives (Springer Series in Statistics)

Description

Thoroughly revised and updated with the latest results, this Third Edition provides an up-to-date account of the theory and applications of linear models. The authors present a unified theory of inference from linear models and its generalizations with minimal assumptions. They not only use least squares theory, but also alternative methods of estimation and testing based on convex loss functions and general estimating equations.

Some of the highlights you’ll discover in this text include sensitivity analysis and model selection, an analysis of incomplete data, and an analysis of categorical data based on a unified presentation of generalized linear models. You’ll also find an extensive appendix on matrix theory that is particularly useful for researchers in econometrics, engineering, and optimization theory.

This text is recommended for courses in statistics at the graduate level. It also serves as a supplemental text for other courses in which linear models play a role.