Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/104335
Title: Global and local distance-based generalized linear models
Author: Boj del Val, Eva
Caballé Mestres, Adrià
Delicado, Pedro
Esteve, Anna
Fortiana Gregori, Josep
Keywords: Models lineals (Estadística)
Estimació d'un paràmetre
Linear models (Statistics)
Parameter estimation
Issue Date: Mar-2016
Publisher: Springer Verlag
Abstract: This paper introduces local distance-based generalized linear models. These models extend (weighted) distance-based linear models first to the generalized linear model framework. Then, a nonparametric version of these models is proposed by means of local fitting. Distances between individuals are the only predictor information needed to fit these models. Therefore, they are applicable, among others, to mixed (qualitative and quantitative) explanatory variables or when the regressor is of functional type. An implementation is provided by the R package dbstats, which also implements other distance-based prediction methods. Supplementary material for this article is available online, which reproduces all the results of this article.
Note: Versió postprint del document publicat a: https://doi.org/10.1007/s11749-015-0447-1
It is part of: TEST, 2016, vol. 25, p. 170-195
Related resource: https://doi.org/10.1007/s11749-015-0447-1
URI: http://hdl.handle.net/2445/104335
ISSN: 1133-0686
Appears in Collections:Articles publicats en revistes (Matemàtica Econòmica, Financera i Actuarial)

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