NONPARAMETRIC REGRESSION WITH ERROR-IN-VARIABLES MODEL BASED ON DIFFERENT KERNEL FUNCTIONS
Abstract
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.
Keywords
Error in variables, kernel smoothing, nonparametric regression, kernel functions
References
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