Methodology for nonparametric bias reduction in kernel regression estimation

Slaoui Yousri ()
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Slaoui Yousri: CNRS, LMA, Université de Poitiers, Poitiers, France

Monte Carlo Methods and Applications, 2023, vol. 29, issue 1, 55-77

Abstract: In this paper, we propose and investigate two new kernel regression estimators based on a bias reduction transformation technique. We study the properties of these estimators and compare them with Nadaraya–Watson’s regression estimator and Slaoui’s (2016) regression estimator. It turns out that, with an adequate choice of the parameters of the two proposed estimators, the rate of convergence of two estimators will be faster than the two classical estimators, and the asymptotic MISE (mean integrated squared error) will be smaller than the two classical estimators. We corroborate these theoretical results through simulations and a real Malaria dataset.

Keywords: Nonparametric regression; stochastic approximation algorithm; smoothing; curve fitting; bias reduction (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:mcmeap:v:29:y:2023:i:1:p:55-77:n:4

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DOI: 10.1515/mcma-2022-2130

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