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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, 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 : 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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https://hal.inria.fr/hal-01206787
Contributor : Sophie Dabo-Niang <>
Submitted on : Tuesday, September 29, 2015 - 3:57:53 PM
Last modification on : Saturday, February 27, 2021 - 3:05:42 AM

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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, Pushpa Publishing House, 2015, 46 (2), pp.119 - 158. ⟨10.17654/ADASAug2015_119_158⟩. ⟨hal-01206787⟩

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