Linear Models and Generalizations
Least Squares and Alternatives
(Sprache: Englisch)
Revised and updated with the latest results, this Third Edition explores the theory and applications of linear models. The authors present a unified theory of inference from linear models and its generalizations with minimal assumptions. They not only use...
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Revised and updated with the latest results, this Third Edition explores the theory and applications of linear models. The authors present a unified theory of inference from linear models and its generalizations with minimal assumptions. They not only use least squares theory, but also alternative methods of estimation and testing based on convex loss functions and general estimating equations. Highlights of coverage include sensitivity analysis and model selection, an analysis of incomplete data, an analysis of categorical data based on a unified presentation of generalized linear models, and an extensive appendix on matrix theory.
Klappentext zu „Linear Models and Generalizations “
Revised and updated with the latest results, this Third Edition explores the theory and applications of linear models. The authors present a unified theory of inference from linear models and its generalizations with minimal assumptions. They not only use least squares theory, but also alternative methods of estimation and testing based on convex loss functions and general estimating equations. Highlights of coverage include sensitivity analysis and model selection, an analysis of incomplete data, an analysis of categorical data based on a unified presentation of generalized linear models, and an extensive appendix on matrix theory.
Inhaltsverzeichnis zu „Linear Models and Generalizations “
1. Introduction2. 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
Autoren-Porträt von C. Radhakrishna Rao, Helge Toutenburg, Shalabh
Thoroughly revised and updated with the latest results, this Third Edition provides an account of the theory and applications of linear models. The authors present a unified theory of inference from linear models and its generalizations with minimal assumptions. They not only use least squares theory, but also alternative methods of estimation and testing based on convex loss functions and general estimating equations. Highlights include sensitivity analysis and model selection, an analysis of incomplete data, and an analysis of categorical data based on a unified presentation of generalized linear models. There is also an extensive appendix on matrix theory that is particularly useful for researchers in econometrics, engineering, and optimization theory. This text is recommended for courses in statistics at the graduate level as well as for other courses in which linear models play a role.
Bibliographische Angaben
- Autoren: C. Radhakrishna Rao , Helge Toutenburg , Shalabh
- 2007, 3rd extend. ed., 572 Seiten, Maße: 16 x 24,1 cm, Gebunden, Englisch
- Mitarbeit:Schomaker, M.
- Verlag: Springer
- ISBN-10: 3540742263
- ISBN-13: 9783540742265
- Erscheinungsdatum: 12.10.2007
Sprache:
Englisch
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