Artículo
Nonparametric prediction for univariate spatial data: methods and applications
Fecha de publicación:
04/2023
Editorial:
John Wiley & Sons
Revista:
Papers in Regional Science
ISSN:
1435-5957
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
We introduce five nonparametric kriging-type predictors for spatial data where only the variable of interest, without covariates, is recorded. The proposed methods seek to fully exploit the information contained in the spatial closeness and also in the similarity between neighbourhoods of the variable of interest. This is managed using different combinations of kernels (one or two kernels), and different combinations of distances (multiplicative and additive). The good performance of the proposed methods is shown via simulation studies and housing price prediction applications.
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Articulos(CCT - SANTA FE)
Articulos de CTRO.CIENTIFICO TECNOL.CONICET - SANTA FE
Articulos de CTRO.CIENTIFICO TECNOL.CONICET - SANTA FE
Citación
García Arancibia, Rodrigo; Llop Orzan, Pamela Nerina; Lovatto, Mariel Guadalupe; Nonparametric prediction for univariate spatial data: methods and applications; John Wiley & Sons; Papers in Regional Science; 102; 3; 4-2023; 635-672
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