Linear models and generalizations
Linear models and generalizations least squares and alternatives / [electronic resource] :
C. Radhakrishna Rao ... [et al.] ; with contributions by Michael Schomaker.
- 3rd extended ed.
- Berlin ; New York : Springer, c2008.
- 1 online resource (xix, 570 p.) : ill.
- Springer series in statistics. .
- Springer series in statistics. .
Includes bibliographical references (p. [539]-562) and index.
Online version restricted to NUS staff and students only through NUSNET.
"This book 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 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. 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." "For this third edition the text has been extensively revised and contains the latest developments in the area of linear models. Many new topics like regression techniques, nonparametric regression, bagging, boosting, regression trees and full likelihood methods for correlated response in categorical data have been included."--Jacket.
Mode of access: World Wide Web.
System requirements: Internet connectivity; World Wide Web browser.
9783540742272 3540742271 9783540742265 3540742263 6611355308 9786611355302
978-3-540-74226-5 Springer
Linear models (Statistics)
Includes bibliographical references (p. [539]-562) and index.
Online version restricted to NUS staff and students only through NUSNET.
"This book 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 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. 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." "For this third edition the text has been extensively revised and contains the latest developments in the area of linear models. Many new topics like regression techniques, nonparametric regression, bagging, boosting, regression trees and full likelihood methods for correlated response in categorical data have been included."--Jacket.
Mode of access: World Wide Web.
System requirements: Internet connectivity; World Wide Web browser.
9783540742272 3540742271 9783540742265 3540742263 6611355308 9786611355302
978-3-540-74226-5 Springer
Linear models (Statistics)