Linear Models and Generalizations Least Squares and Alternatives/ [electronic resource]:
by C. Radhakrishna Rao, Shalabh, Helge Toutenburg, Christian Heumann.
- Third Extended Edition.
- Berlin, Heidelberg: Springer Berlin Heidelberg, 2008.
- digital.
- Springer Series in Statistics, 0172-7397 .
- Springer series in statistics. .
1. Introduction -- 2. The Simple Linear Regression Model -- 3. The Multiple Linear Regression Model -- 4. The Generalized Linear Regression Model -- 5. Exact and Stochastic Linear Restrictions -- 6. Prediction Problems in the Generalized Regression Model -- 7. Sensitivity Analysis -- 8. Analysis of Incomplete Data Sets -- 9. Robust Regression -- 10. Models for Categorical Response Variables -- Fitting Smooth Functions -- Appendix A: Matrix Algebra .
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 and its generalizations 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: sensitivity analysis and model selection, analysis of incomplete data, analysis of categorical data based on a unified presentation of generalized linear models including GEE- and full likelihood methods for correlated response, an extensive appendix on matrix theory, useful to researchers in econometrics, engineering and optimization theory. For this third edition the text has been extensively revised and contains the latest developments in the area of linear models .
9783540742272
10.1007/978-3-540-74227-2 doi
Statistics. Computer science. Distribution (Probability theory) Mathematical statistics Economics, Mathematical.