Estimating the smoothing parameter in generalized spline-based regression: I. Cross-validatory criteria for binary data using small sample sizes
type: Item
instance of: scholarly article
title: Estimating the smoothing parameter in generalized spline-based regression: I. Cross-validatory criteria for binary data using small sample sizes
zbMATH Open document ID: 1007.62036
published in: Computational Statistics
publication date: 2003-03-09T00:00:00Z
review text: The author considers a nonparametric regression model in which the distribution of data is described by a one-parameter exponential family density and the estimated regression function links the covariates with the parameter of the family. It is supposed that this function is smooth enough. A spline-smoothing technique is used for estimation. Different methods of smoothing parameter validation are considered, such as full and robustified cross-validation. Results of simulations are presented for the estimation of different functions, e.g., \(h(s,t)=15(\exp(-s^2-t^2-st))-11\).
Mathematics Subject Classification ID: 62G08
Mathematics Subject Classification ID: 62-08
zbMATH DE Number: 1878599
zbMATH Keywords: binary responses
zbMATH Keywords: robustified cross-validation
author: Angelika van der Linde
MaRDI profile type: Publication
full work available at URL: s001800100051
OpenAlex ID: W1966025611
Wikidata QID: Q108929316
DOI: 10.1007/S001800100051
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description: scientific article; zbMATH DE number 1878599