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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
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 : 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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Contributor : Sophie Dabo-Niang <>
Submitted on : Monday, October 28, 2019 - 8:39:53 AM
Last modification on : Friday, November 27, 2020 - 2:18:03 PM




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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