Choice of Bandwidth for Nonparametric Regression Models using Kernel Smoothing: A Simulation Study


In this study, kernel smoothing method is considered in the estimation of nonparametric regression models. A crucial step in the implementation of this method is to select a proper bandwidth (smoothing parameter). In an attempt to address the specification of amount of smoothing, this article provides a comparative study of different methods (or criteria) for choosing the smoothing parameter. Given the need of automatic data-driven smoothing parameter selectors for applied statistics, this study is focused to explain and compare these methods. In this context, we generalized the selection methods used in the smoothing spline method for kernel smoothing. In order to explore and compare the performance of these methods, a simulation study is performed for data sets with different sample sizes. As a result of simulation, the appropriate selection criteria are provided for a suitable smoothing parameter selection.


Kernel smoothing; Smoothing spline; Nonparametric regression; Bandwidth; Selection method.

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