Bias Reduction for Nonparametric Density Estimator with Standard Kernels
學年 115
學期 1
出版（發表）日期 2026-09-30
作品名稱 Bias Reduction for Nonparametric Density Estimator with Standard Kernels
作品名稱（其他語言）
著者 Wen-Shuenn Deng; Yi-Chen Lin; Kung-Han Yang; Chen-Chin Cheng
單位
出版者
著錄名稱、卷期、頁數 International Journal of Statistics and Probability 15(3) ,p.5-23
摘要 Kernel density estimation (KDE) is a widely used nonparametric tool for recovering unknown probability density functions without strong distributional assumptions. However, even well-tuned kernel density estimators often exhibit non-negligible bias, which can potentially affect the accuracy of subsequent statistical inference. In this paper, we adapt the bias-reduction approach of Cheng et al. (2018), originally proposed for kernel regression, to the classical kernel density estimator. The resulting method reduces the order of magnitude of the asymptotic bias while maintaining the variance at the same order as that of the standard estimator. Unlike traditional bias-reduction strategies based on higher-order kernels or variable kernels, our approach has a simple computational structure and does not require oscillatory kernel functions or pilot estimates for local bandwidths. We derive the theoretical properties of the proposed estimator and assess its performance through simulation studies and two real-data applications. The results show that the bias-reduced estimator often achieves lower mean integrated squared errors than the standard KDE, particularly for finite samples and high-curvature target densities.
關鍵字 Kernel density estimate;Bias reduction;Mean integrated squared error;Higher-order kernels;Variable kernels;Ordinary least squares
語言 en_US
ISSN 1927-7032
期刊性質 國外
收錄於
產學合作
通訊作者
審稿制度 是
國別 CAN
公開徵稿
出版型式 ,電子版,紙本
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/129955 )