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Linear models and generalizations : least squares and alternatives / C. Radhakrishna Rao ... [et al.]

Contributor(s): Rao, C. Radhakrishna.
Material type: TextTextSeries: Springer series in statistics.Publisher: New York : Springer, 2008Edition: 3rd extended ed.Description: xix, 570 p. : ill. ; 25 cm.ISBN: 9783540742265 ; 9780387988481.Subject(s): Mathematical satistics | Linear models (Statistics)DDC classification: 519.5 Online resources: Ebook Fulltext
Contents:
Table of contents 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
Summary: "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 Read more...
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Item type Current location Collection Call number Copy number Status Date due Barcode Item holds
E-Book E-Book EWU Library
E-book
Non-fiction 519.5 LIN 2008 (Browse shelf) Not for loan
Text Text EWU Library
Reserve Section
Non-fiction 519.5 LIN 2008 (Browse shelf) C-1 Not For Loan 27312
Text Text EWU Library
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Non-fiction 519.5 LIN 2008 (Browse shelf) C-2 Available 28167
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Print version:
Linear models and generalizations.
Berlin ; New York : Springer, ©2008
(OCoLC)173807301.

Includes Bibliographical References and Index.

Table of contents 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. 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 Read more...

Applied Statistics

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