We have attempted to provide a unified theory of inference from linear models with minimal assumptions. The matrix theory of the last ten years has produced a series of fundamental results about the definiteness of matrices, especially for the differences of matrices, which enable superiority comparisons of two biased estimates to be made for the first time. A relatively extensive chapter on matrix theory (Appendix A) provides the necessary tools for proving theorems discussed in the text and offers a selection of classical and modern algebraic results that are useful in research work in econometrics, engineering, and optimization theory. Some of the highlights in this book are as follows. The book can be used as a text for courses in statistics at the graduate level and as an accompanying text for courses in other areas. It gives an up-to-date account of the theory and applications of linear models. The book is based on several years of experience of both authors in teaching linear models at various levels. Linear Models: Least Squares and Alternatives, Second Edition
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