期刊名称：Journal of Modern Applied Statistical Methods
出版年度：2010
卷号：9
期号：1
页码：26
出版社：Wayne State University
摘要：Nonparametric kernel density estimation method does not make any assumptions regarding the functional form of curves of interest; hence it allows flexible modeling of data. A crucial problem in kernel density estimation method is how to determine the bandwidth (smoothing) parameter. This article examines the most important bandwidth selection methods, in particular, least squares cross-validation, biased crossvalidation, direct plug-in, solve-the-equation rules and contrast methods. Methods are described and expressions are presented. The main practical contribution is a comparative simulation study that aims to isolate the most promising methods. The performance of each method is evaluated on the basis of the mean integrated squared error for small-to-moderate sample size. Simulation results show that the contrast method is the most promising methods based on the simulated families considered.
关键词：Probability Density Function; Bandwidth; Least Squares Cross-Validation; Biased Cross-Validation; Contrast Method; Direct Plug-In; Solve-The-Equation Rules
