Eskişehir Teknik Üniversitesi Bilim ve Teknoloji Dergisi B - Teorik Bilimler 2667-419X Eskisehir Technical University 10.20290/estubtdb.1015940 NONPARAMETRIC REGRESSION WITH ERROR-IN-VARIABLES MODEL BASED ON DIFFERENT KERNEL FUNCTIONS NONPARAMETRIC REGRESSION WITH ERROR-IN-VARIABLES MODEL BASED ON DIFFERENT KERNEL FUNCTIONS https://orcid.org/0000-0001-8393-1270 Aydın Dursun MUĞLA SITKI KOÇMAN ÜNİVERSİTESİ https://orcid.org/0000-0002-9871-4700 Yılmaz Ersin MUGLA UNIVERSITY 12 24 2021 9 Iconat Special Issue 2021 94 102 10 28 2021 11 29 2021 Copyright © 2010, Eskişehir Teknik Üniversitesi Bilim ve Teknoloji Dergisi B - Teorik Bilimler 2010 Eskişehir Teknik Üniversitesi Bilim ve Teknoloji Dergisi B - Teorik Bilimler

Estimation of error-invariable models is a specific problem in different fields such as medicine, economics, industry, and biostatistics. The main different between classical regression and error-in-variable models is that explanatory variables involve random error terms. Therefore, classical estimation methods that do not include the necessary adjustments for the contaminated explanatory variables give biased results. Regarding the error-in variables, there are important studied in the literature such as [1], [2], [3], [4], [5] and [6]. In this paper, nonparametric regression with measurement error is considered and estimated by kernel smoothing estimator which is studied detailed by [6]. This paper differs from their study with the idea of using two different kernel functions to compared them on quality of estimations. These functions are suitable for different error behaviors (see [7]). The goal of the paper is encouraged by a Monte Carlo simulation study and results are presented.

Estimation of error-invariable models is a specific problem in different fields such as medicine, economics, industry, and biostatistics. The main different between classical regression and error-in-variable models is that explanatory variables involve random error terms. Therefore, classical estimation methods that do not include the necessary adjustments for the contaminated explanatory variables give biased results. Regarding the error-in variables, there are important studied in the literature such as [1], [2], [3], [4], [5] and [6]. In this paper, nonparametric regression with measurement error is considered and estimated by kernel smoothing estimator which is studied detailed by [6]. This paper differs from their study with the idea of using two different kernel functions to compared them on quality of estimations. These functions are suitable for different error behaviors (see [7]). The goal of the paper is encouraged by a Monte Carlo simulation study and results are presented.

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