Efficient Bandwidth Selection for Kernel Density Estimation

We consider bandwidth selection for kernel density estimation. The performance of kernel density estimator heavily relies on the quality of the bandwidth. In this paper, we propose an efficient plug-in kernel density estimator which first perturbs the bandwidth to estimate the optimal bandwidth, followed by applying a kernel density estimator with the estimated optimal bandwidth. The proposed method utilizes the zeroth-order information of kernel function and has a faster convergence rate than other plug-in methods in existing literature. Simulation results demonstrate superior finite sample performance and robustness of the proposed method.