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Journal Articles Journal of Nonparametric Statistics Year : 2016

Nonparametric prediction in the multivariate spatial context

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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.
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Dates and versions

hal-01425932 , version 1 (04-01-2017)

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Sophie Dabo-Niang, Camille Ternynck, Anne-Françoise Yao. Nonparametric prediction in the multivariate spatial context. Journal of Nonparametric Statistics, 2016, 28 (2), pp.428-458. ⟨10.1080/10485252.2016.01.007⟩. ⟨hal-01425932⟩
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