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Zusatztext "...provides an excellent introduction."- Short Book Reviews of the ISI"It is well written and very reliable."- Technometrics Informationen zum Autor P.J. Green, Bristol Univesity. Bernard. W. Silverman St. Peters College, Oxford. Klappentext 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. Zusammenfassung This study of nonparametric regression and generalized linear models contains chapters on approaches to regression, roughness penalties, extensions of the roughness penalty approach, computing the estimates, interpolating and smoothing splines, one-dimensional case, partial splines, and more. Inhaltsverzeichnis 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.
