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A kernel spatial density estimation allowing for the analysis of spatial clustering. Application to Monsoon Asia Drought Atlas data

Sophie Dabo-Niang 1, 2 Leila Hamdad 3 Camille Ternynck 1 Anne-Françoise Yao 4
2 MODAL - MOdel for Data Analysis and Learning
Inria Lille - Nord Europe, LPP - Laboratoire Paul Painlevé - UMR 8524, METRICS - Evaluation des technologies de santé et des pratiques médicales - ULR 2694, Polytech Lille - École polytechnique universitaire de Lille, Université de Lille, Sciences et Technologies
Abstract : A nonparametric density estimate that incorporates spatial dependency has not been studied in the literature. In this article, we propose a new spatial density estimator that depends on two kernels: one controls the distance between observations while the other controls the spatial dependence structure. The uniform almost sure convergence of the density estimate is established with the rate of convergence. The consistency of the mode of this kernel density is also studied. Then a spatial hierarchical unsupervised clustering algorithm based on the mode estimate is presented. Some simulations as well as an application to the Monsoon Asia Drought Atlas data illustrate the efficiency of our algorithm, and a comparison of the spatial structures of these data detected by the density estimate and clustering algorithm are done.
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https://hal.univ-lille.fr/hal-01094743
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Soumis le : vendredi 12 décembre 2014 - 21:19:38
Dernière modification le : mercredi 17 novembre 2021 - 12:08:10

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Sophie Dabo-Niang, Leila Hamdad, Camille Ternynck, Anne-Françoise Yao. A kernel spatial density estimation allowing for the analysis of spatial clustering. Application to Monsoon Asia Drought Atlas data. Stochastic Environmental Research and Risk Assessment, Springer Verlag (Germany), 2014, 28, pp.2075-2099. ⟨10.1007/s00477-014-0903-6⟩. ⟨hal-01094743⟩

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