Semiparametric regression

David Ruppert, M.P. Wand - Collection Cambridge series in statistical and probabilistic mathematics

402 pages, parution le 15/09/2003

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Résumé

Semiparametric regression is concerned with the flexible incorporation of non-linear functional relationships in regression analyses. Any application area that benefits from regression analysis can also benefit from semiparametric regression. Assuming only a basic familiarity with ordinary parametric regression, this user-friendly book explains the techniques and benefits of semiparametric regression in a concise and modular fashion. The authors make liberal use of graphics and examples plus case studies taken from environmental, financial, and other applications. They include practical advice on implementation and pointers to relevant software. The book is suitable as a textbook for students with little background in regression as well as a reference book for statistically oriented scientists such as biostatisticians, econometricians, quantitative social scientists, epidemiologists, with a good working knowledge of regression and the desire to begin using more flexible semiparametric models. Even experts on semiparametric regression should find something new here.

Sommaire

Introduction
Parametric regression
Scatterplot smoothing
Mixed models
Automatic scatterplot smoothing
Inference
Simple semiparametric models
Additive models
Semiparametric mixed models
Generalized parametric regression
Generalized additive models
Interaction models
Bivariate smoothing
Variance function estimation
Measurement error
Bayesian semiparametric regression
Spatially adaptive smoothing
Analyses
Epilogue
Technical complements
Computational issues

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Caractéristiques techniques

	PAPIER
Éditeur(s)	Cambridge University Press
Auteur(s)	David Ruppert, M.P. Wand
Collection	Cambridge series in statistical and probabilistic mathematics
Parution	15/09/2003
Nb. de pages	402
Format	17,7 x 25,4
Couverture	Broché
Poids	698g
Intérieur	Noir et Blanc
EAN13	9780521785167
ISBN13	978-0-521-78516-7