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

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. Some of the highlights include: A special emphasis on sensitivity analysis and model selection; a chapter devoted to the analysis of categorical data based on logit, loglinear, and logistic regression models; a chapter devoted to incomplete data sets; an extensive appendix on matrix theory, useful to researchers in econometrics, engineering, and optimization theory. The material covered will be invaluable not only to graduate students, but also to research workers and consultants in statistics.