Global and local distance-based generalized linear models

Eva Boj, Adrià Caballé (), Pedro Delicado, Anna Esteve () and Josep Fortiana ()

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2016, vol. 25, issue 1, 170-195

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. Copyright Sociedad de Estadística e Investigación Operativa 2016

Keywords: Distance-based prediction; Functional data analysis; Generalized linear model; Iteratively weighted least squares; Local likelihood; R package dbstats; 62G08; 62J12 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s11749-015-0447-1

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