Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1149182 | Journal of Statistical Planning and Inference | 2010 | 15 Pages |
Abstract
In this paper, we investigate a nonparametric robust estimation for spatial regression. More precisely, given a strictly stationary random field Zi=(Xi,Yi)i∈NNN≥1Zi=(Xi,Yi)i∈NNN≥1, we consider a family of robust nonparametric estimators for a regression function based on the kernel method. Under some general mixing assumptions, the almost complete consistency and the asymptotic normality of these estimators are obtained. A robust procedure to select the smoothing parameter adapted to the spatial data is also discussed.
Keywords
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Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
Abdelkader Gheriballah, Ali Laksaci, Rachida Rouane,