An Efficient Algorithm for the Least-Squares Cross-Validation with Symmetric and Polynomial Kernels

Abstract

The least-squares cross-validation is a completely automatic method for choosing the smoothing parameter in probability density estimation but this method consume large amounts of computer time. This article concerns an efficient computational algorithm for this method when the kernel is symmetric and polynomial functions.

Keywords:

• probability density estimation
• least-squares cross-validation
• convolution
• symmetric and polynomial kernels