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Nonparametric prediction in the multivariate spatial context

Sophie Dabo-Niang 1, 2 Camille Ternynck 3 Anne-Françoise Yao 4
2 MODAL - MOdel for Data Analysis and Learning
LPP - Laboratoire Paul Painlevé - UMR 8524, Université de Lille, Sciences et Technologies, Inria Lille - Nord Europe, METRICS - Evaluation des technologies de santé et des pratiques médicales - ULR 2694, Polytech Lille - École polytechnique universitaire de Lille
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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https://hal.inria.fr/hal-01425932
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Submitted on : Wednesday, January 4, 2017 - 8:14:21 AM
Last modification on : Tuesday, December 7, 2021 - 1:14:02 PM

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

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