Nonparametric relative recursive regression

Author:

Slaoui Yousri1,Khardani Salah2

Affiliation:

1. Univ. de Poitiers , Lab. de Mathématiques et App. , Futuroscope Chasseneuil , France

2. Laboratoire des Réseaux Intelligents et Nanotechnologie , Ecole Nationale des Sciences et Technologies Avancées à Borj-Cédria , Tunisia

Abstract

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.

Publisher

Walter de Gruyter GmbH

Subject

Applied Mathematics,Modelling and Simulation,Statistics and Probability

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