Buch, Englisch, 572 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 885 g
Least Squares and Alternatives
Buch, Englisch, 572 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 885 g
Reihe: Springer Series in Statistics
ISBN: 978-3-642-09353-1
Verlag: Springer
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.
Zielgruppe
Research
Fachgebiete
- Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
- Mathematik | Informatik Mathematik Operations Research Spieltheorie
- Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
- Mathematik | Informatik EDV | Informatik Informatik
- Mathematik | Informatik Mathematik Stochastik Stochastische Prozesse
- Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Ökonometrie
- Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
Weitere Infos & Material
The Simple Linear Regression Model.- The Multiple Linear Regression Model and Its Extensions.- The Generalized Linear Regression Model.- Exact and Stochastic Linear Restrictions.- Prediction in the Generalized Regression Model.- Sensitivity Analysis.- Analysis of Incomplete Data Sets.- Robust Regression.- Models for Categorical Response Variables.
