Tilted Nonparametric Regression Function Estimation

This paper provides the theory about the convergence rate of the tilted version of linear smoother. We study tilted linear smoother, a nonparametric regression function estimator, which is obtained by minimizing the distance to an infinite order flat-top trapezoidal kernel estimator. We prove that t...

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Bibliographic Details
Published in:arXiv.org 2021-02
Main Authors: Boroumand, Farzaneh, Shakeri, Mohammad T, Kordzakhia, Nino, Salehi, Mahdi, Doosti, Hassan
Format: Article
Language:English
Subjects:
Curve fitting
Estimators
Kernels
Regression analysis
Online Access:Get full text
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Summary:This paper provides the theory about the convergence rate of the tilted version of linear smoother. We study tilted linear smoother, a nonparametric regression function estimator, which is obtained by minimizing the distance to an infinite order flat-top trapezoidal kernel estimator. We prove that the proposed estimator achieves a high level of accuracy. Moreover, it preserves the attractive properties of the infinite order flat-top kernel estimator. We also present an extensive numerical study for analysing the performance of two members of the tilted linear smoother class named tilted Nadaraya-Watson and tilted local linear in the finite sample. The simulation study shows that tilted Nadaraya-Watson and tilted local linear perform better than their classical analogs in some conditions in terms of Mean Integrated Squared Error (MISE). Finally, the performance of these estimators as well as the conventional estimators were illustrated by curve fitting to COVID-19 data for 12 countries and a dose-response data set.
ISSN:2331-8422