Cross Validation Bandwidth Selection for Derivatives of Multidimensional Densities

21 Pages Posted: 4 Dec 2014

Date Written: October 2, 2014

Abstract

Little attention has been given to the effect of higher order kernels for bandwidth selection for multidimensional derivatives of densities. This paper investigates the extension of cross validation methods to higher dimensions for the derivative of an unconditional joint density. I present and derive different cross validation criteria for arbitrary kernel order and density dimension, and show consistency of the estimator. Doing a Monte Carlo simulation study for various orders of kernels in the Gaussian family and additionally comparing a weighted integrated square error criterion, I find that higher order kernels become increasingly important as the dimension of the distribution increases. I find that standard cross validation selectors generally outperform the weighted integrated square error cross validation criteria. Using the infinite order Dirichlet kernel tends to have the best results.

Suggested Citation

Baird, Matthew, Cross Validation Bandwidth Selection for Derivatives of Multidimensional Densities (October 2, 2014). RAND Working Paper Series WR-1060. Available at SSRN: https://ssrn.com/abstract=2533259 or http://dx.doi.org/10.2139/ssrn.2533259

Matthew Baird (Contact Author)

RAND Corporation ( email )

1776 Main Street
P.O. Box 2138
Santa Monica, CA 90407-2138
United States

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
18
Abstract Views
334
PlumX Metrics