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Kernel regression estimation with errors-in-variables for random fields

Sophie Dabo-Niang 1 Baba Thiam 2 
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 : In this paper, we investigate kernel regression estimation when the data are contaminated by measurement errors in the context of random fields. We establish sharp rate of weak and strong convergence of the kernel regression estimator under both the ordinary smooth and super-smooth assumptions. Numerical studies were carried out in order to illustrate the performance of the estimator with simulated data.
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https://hal.inria.fr/hal-02334993
Contributor : Sophie Dabo-Niang Connect in order to contact the contributor
Submitted on : Monday, October 28, 2019 - 8:39:53 AM
Last modification on : Wednesday, March 23, 2022 - 3:51:06 PM

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Sophie Dabo-Niang, Baba Thiam. Kernel regression estimation with errors-in-variables for random fields. Afrika Matematika, Springer, 2020, 31, pp.29-56. ⟨10.1007/s13370-019-00654-7⟩. ⟨hal-02334993⟩

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