Nonparametric Regression and Generalized Linear Models: A roughness penalty approach
Nonparametric Regression and Generalized Linear Models focuses on the roughness penalty method of nonparametric smoothing and shows how this technique provides a unifying approach to a wide range of smoothing problems. The emphasis is methodological rather than theoretical, and the authors concentrate on statistical and computation issues. Real data examples are used to illustrate the various methods and to compare them with standard parametric approaches. The mathematical treatment is self-contained and depends mainly on simple linear algebra and calculus. This monograph will be useful both as a reference work for research and applied statisticians and as a text for graduate students.
1136630041
Nonparametric Regression and Generalized Linear Models: A roughness penalty approach
Nonparametric Regression and Generalized Linear Models focuses on the roughness penalty method of nonparametric smoothing and shows how this technique provides a unifying approach to a wide range of smoothing problems. The emphasis is methodological rather than theoretical, and the authors concentrate on statistical and computation issues. Real data examples are used to illustrate the various methods and to compare them with standard parametric approaches. The mathematical treatment is self-contained and depends mainly on simple linear algebra and calculus. This monograph will be useful both as a reference work for research and applied statisticians and as a text for graduate students.
240.0 In Stock
Nonparametric Regression and Generalized Linear Models: A roughness penalty approach

Nonparametric Regression and Generalized Linear Models: A roughness penalty approach

Nonparametric Regression and Generalized Linear Models: A roughness penalty approach
Add to Wishlist
Nonparametric Regression and Generalized Linear Models: A roughness penalty approach

Nonparametric Regression and Generalized Linear Models: A roughness penalty approach

Overview

Nonparametric Regression and Generalized Linear Models focuses on the roughness penalty method of nonparametric smoothing and shows how this technique provides a unifying approach to a wide range of smoothing problems. The emphasis is methodological rather than theoretical, and the authors concentrate on statistical and computation issues. Real data examples are used to illustrate the various methods and to compare them with standard parametric approaches. The mathematical treatment is self-contained and depends mainly on simple linear algebra and calculus. This monograph will be useful both as a reference work for research and applied statisticians and as a text for graduate students.

Product Details

ISBN-13: 9781040072783
Publisher: CRC Press
Publication date: 05/01/1993
Series: Chapman & Hall/CRC Monographs on Statistics and Applied Probability
Sold by: Barnes & Noble
Format: eBook
Pages: 184
File size: 1 MB

About the Author

P.J. Green, Bristol University. Bernard. W. Silverman St. Peters College, Oxford.

Table of Contents

Preface. Introduction. Approaches to Regression. Roughness Penalties. Extensions of the Roughness Penalty Approach. Computing the Estimates. Further Reading. Interpolating and Smoothing Splines. One-Dimensional Case: Further Topics. Partial Splines. Generalized Linear Models. Extending the Model. Thin Plate Splines. Available Software. Reference. Author Index. Subject Index.
From the B&N Reads Blog
Page 1 of

Related Subjects

Science & Technology
Mathematics
Statistics
Science & Technology
Mathematics
Statistics

Customer Reviews