Non-parametric level set estimation for spatial data

Sophie Dabo-Niang 1 Guy-Martial Nkiet 2 Stéphane Bouka 3
1 MODAL - MOdel for Data Analysis and Learning
LPP - Laboratoire Paul Painlevé - UMR 8524, Inria Lille - Nord Europe, CERIM - Santé publique : épidémiologie et qualité des soins-EA 2694, Polytech Lille, Université de Lille 1, IUT’A
Abstract : A non-parametric level set estimator of the density of a stationary d-dimensional spatial process is proposed. The estimator is deduced from a non-parametric kernel density estimator. Berry-Esseen bounds are established and used to give consistency results of the kernel level set estimation, derived from that of the kernel density estimate under some mild conditions.
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Article dans une revue
Advances and Applications in Statistics, 2015, 46 (2), pp.119 - 158. 〈10.17654/ADASAug2015_119_158〉
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https://hal.inria.fr/hal-01206787
Contributeur : Sophie Dabo-Niang <>
Soumis le : mardi 29 septembre 2015 - 15:57:53
Dernière modification le : mercredi 25 avril 2018 - 14:23:16

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Sophie Dabo-Niang, Guy-Martial Nkiet, Stéphane Bouka. Non-parametric level set estimation for spatial data. Advances and Applications in Statistics, 2015, 46 (2), pp.119 - 158. 〈10.17654/ADASAug2015_119_158〉. 〈hal-01206787〉

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