Methodology for nonparametric bias reduction in kernel regression estimation

IF 0.8 Q3 STATISTICS & PROBABILITY
Y. Slaoui
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引用次数: 0

Abstract

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.
核回归估计中的非参数偏差约简方法
摘要在本文中,我们提出并研究了两种新的基于偏差减少变换技术的核回归估计量。我们研究了这些估计量的性质,并将其与Nadaraya–Watson回归估计量和Slaoui(2016)回归估计量进行了比较。结果表明,在适当选择两个估计量的参数的情况下,两个估计的收敛速度将快于两个经典估计量,并且渐近均方误差将小于两个经典估算量。我们通过模拟和真实的疟疾数据集证实了这些理论结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Monte Carlo Methods and Applications
Monte Carlo Methods and Applications STATISTICS & PROBABILITY-
CiteScore
1.20
自引率
22.20%
发文量
31
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