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

Contributor(s): Rao, C. Radhakrishna Material type: Text

TextLanguage: English Series: Springer series in statisticsPublication details: New York : Springer, 2008. Edition: 3rd extended edDescription: xix, 570 p. : ill. ; 25 cmISBN: 9783540742265 ; 9780387988481Subject(s): Mathematical satistics | Linear models (Statistics)DDC classification: 519.5 Online resources: Ebook Fulltext

Contents:

TOC 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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Title notes ( 6 )
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Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Barcode
E-Book	EWU Library E-book	Non-fiction	519.5 LIN 2008 (Browse shelf(Opens below))		Not for loan
Text	EWU Library Reserve Section	Non-fiction	519.5 LIN 2008 (Browse shelf(Opens below))	C-1	Not For Loan	27312
Text	EWU Library Circulation Section	Non-fiction	519.5 LIN 2008 (Browse shelf(Opens below))	C-2	Available	28167

Total holds: 0

Includes Bibliographical References and Index.

TOC 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...

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