Genetic algorithms for the selection of smoothing parameters in additive models
type: Item
instance of: scholarly article
title: Genetic algorithms for the selection of smoothing parameters in additive models
zbMATH Open document ID: 1113.62049
author: Rüdiger Krause
author: Gerhard Tutz
published in: Computational Statistics
publication date: 2007-05-29T00:00:00Z
review text: A nonparametric additive regression model is considered of the form \[ y_t=\beta_0+\sum_{j=1}^p f_j(x_{ij})+\varepsilon_i, \] where \(y_i\) is the response, \(x_{ij}\) are regressors, and \(f_j\) are unknown regression functions to be estimated via local spline smoothing. A genetic type algorithm is proposed for the selection of a smoothing parameters set for which the smoothed curves minimize the improved AIC criterion. The algorithm uses an improved arithmetical crossover operator described in the paper. The obtained algorithm is compared with Bayesian P-splines, adaptive regression, and some other alternative approaches via simulations.
Mathematics Subject Classification ID: 62G08
Mathematics Subject Classification ID: 90C59
Mathematics Subject Classification ID: 62-08
zbMATH DE Number: 5158586
zbMATH Keywords: nonparametric additive regression model
zbMATH Keywords: arithmetical crossover operator
zbMATH Keywords: Akaike criterion
describes a project that uses: FITPACK
describes a project that uses: Genocop
MaRDI profile type: Publication
full work available at URL: s00180-006-0248-9
OpenAlex ID: W1973548524
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DOI: 10.1007/S00180-006-0248-9
description: scientific article; zbMATH DE number 5158586