Nonparametric prediction of spatial multivariate data. Issue 2 (2nd April 2016)

• Record Type: Journal Article
• Authors: Dabo-Niang, Sophie; Ternynck, Camille; Yao, Anne-Françoise
• Is Part Of: Journal of nonparametric statistics. Volume 28:Issue 2(2016)
• Page Start: 428
• Page End: 458
• Publication Date: 2016-04-02
• Subjects: random field -- spatial process -- spatial prediction; 62H11 -- 62M30 -- 62M20; Nonparametric statistics -- Periodicals; 519.5
• DOI: 10.1080/10485252.2016.1164313
• Languages: English
• ISSNs: 1048-5252

Abstract

Abstract : This paper investigates a nonparametric spatial predictor of a stationary multidimensional spatial process observed over a rectangular domain. The proposed predictor depends on two kernels in order to control both the distance between observations and that between spatial locations. The uniform almost complete consistency and the asymptotic normality of the kernel predictor are obtained when the sample considered is an alpha-mixing sequence. Numerical studies were carried out in order to illustrate the behaviour of our methodology both for simulated data and for an environmental data set.