Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/75646
Type: Artigo de periódico
Title: Sequential adaptive nonparametric regression via H-splines
Author: Dias, R
Abstract: The hybrid spline method (H-spline) introduced by Dias (1994) is a hybrid method of curve estimation which combines ideas of regression spline and smoothing spline methods. In the context of nonparametric regression and by using basis functions (B-splines), this method is much faster than smoothing spline methods (e.g. (Wahba, 1990)). The H-spline algorithm is designed to compute a solution of the penalized least square problem, where the smoothing parameter is updated jointly with the number of basis functions in a performance-oriented iteration. The algorithm increases the number of basis functions by one until the partial affinity between two consecutive estimates satisfies a constant determined empirically.
Subject: penalized least squares
B-splines
smoothing splines
Hellinger distance
generalized cross validation
hybrid splines
Country: EUA
Editor: Marcel Dekker Inc
Rights: fechado
Identifier DOI: 10.1080/03610919908813562
Date Issue: 1999
Appears in Collections:Artigos e Materiais de Revistas Científicas - Unicamp

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