Linear Models Least Squares and Alternatives

This book provides an up-to-date account of the theory and applications of linear models. It can be used as a text for courses in statistics at the graduate level as well as an accompanying text for other courses in which linear models play a part. The authors present a unified theory of inference from linear models with minimal assumptions, not only through least squares theory, but also using alternative methods of estimation and testing based on convex loss functions and general estimating equations. The bookincludes a discussion of:
-- sensitivity analysis and model selection
-- incomplete data sets including regression diagnostics to identify Non-MCAR-processes
-- the analysis of categorical data based on a unified presentation of generalized linear models including GEE-methods for correlated response.

An extensive appendix on matrix theory will be useful to researchers in econometrics, engineering, and optimization theory.