Linear Models: Least Squares and Alternatives

This text 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 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.; Topics include: sensitivity analysis and model selection; analysis of categorical data based on logic, loglinear and logistic regression models; incomplete data sets; matrix theory; neural networks; and details of software available for the models covered throughout the book.