Nonparametric relative recursive regression

Slaoui Yousri () and Khardani Salah
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Slaoui Yousri: Univ. de Poitiers, Lab. de Mathématiques et App., FuturoscopeChasseneuil, France
Khardani Salah: Laboratoire des Réseaux Intelligents et Nanotechnologie, Ecole Nationale des Sciences et TechnologiesAvancées à Borj-Cédria, Tunisia

Dependence Modeling, 2020, vol. 8, issue 1, 221-238

Abstract: In this paper, we propose the problem of estimating a regression function recursively based on the minimization of the Mean Squared Relative Error (MSRE), where outlier data are present and the response variable of the model is positive. We construct an alternative estimation of the regression function using a stochastic approximation method. The Bias, variance, and Mean Integrated Squared Error (MISE) are computed explicitly. The asymptotic normality of the proposed estimator is also proved. Moreover, we conduct a simulation to compare the performance of our proposed estimators with that of the two classical kernel regression estimators and then through a real Malaria dataset.

Keywords: nonparametric regression; stochastic approximation algorithm; smoothing; curve fitting; relative regression (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:demode:v:8:y:2020:i:1:p:221-238:n:13

DOI: 10.1515/demo-2020-0013

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