-estimation for spatial nonparametric regression

Rongrong Xu and Jinde Wang

Journal of Nonparametric Statistics, 2008, vol. 20, issue 6, 523-537

Abstract: Assuming the structure of a mixing spatial data process {(Yi, Xi), i∈ℝN}, the least absolute deviation (L1) method is proposed to estimate the spatial conditional regression function with the superiority of weakening the influence of outliers and aberrant observations, which appear very often in spatial data. With appropriate choices of the bandwidth under some mild conditions imposed on the spatial process, the asymptotic distributions of the estimators are derived. Three simulation models using L1 and L2 methods respectively show that the L1-estimators are superior to L2-estimators.

Date: 2008
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DOI: 10.1080/10485250801976717

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