Global and local distance-based generalized linear models

Eva Boj ^[1] ; Adrià Caballé ^[3] ; Pedro Delicado ^[2] ; Anna Esteve ^[4] ; Josep Fortiana ^[1]
1. [1] Universitat de Barcelona
  
  Universitat de Barcelona
  
  Barcelona, España
2. [2] Universitat Politècnica de Catalunya
  
  Universitat Politècnica de Catalunya
  
  Barcelona, España
3. [3] University of Edimbourgh
4. [4] Agencia de Salut Pública de Catalunya
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Localización: Test: An Official Journal of the Spanish Society of Statistics and Operations Research, ISSN-e 1863-8260, ISSN 1133-0686, Vol. 25, Nº. 1, 2016, págs. 170-195
Idioma: inglés
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Resumen
- 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.