Yi-Chen Lin

Bias Reduction for Nonparametric Density Estimator with Standard Kernels

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 in...