Numerical performance of Penalized Comparison to Overfitting for multivariate kernel density estimation release_xygtu4lcvfappeqkvcvxiaey4a

by Suzanne Varet, Claire Lacour, Pascal Massart, Vincent Rivoirard

Published in E S A I M: Probability & Statistics by EDP Sciences.

2022  

Abstract

Kernel density estimation is a well known method involving a smooth- ing parameter (the bandwidth) that needs to be tuned by the user. Although this method has been widely used, the bandwidth selection remains a challenging issue in terms of balancing algorithmic performance and statistical relevance. The pur- pose of this paper is to study a recently developed bandwidth selection method, called Penalized Comparison to Overfitting (PCO). We first provide new theo- retical guarantees by proving that PCO performed with non-diagonal bandwidth matrices is optimal in the oracle and minimax approaches. PCO is then compared to other usual bandwidth selection methods (at least those which are implemented in the R-package) for univariate and also multivariate kernel density estimation on the basis of intensive simulation studies. In particular, cross-validation and plug- in criteria are numerically investigated and compared to PCO. The take home message is that PCO can outperform the classical methods without algorithmic additional cost.
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Date   2022-11-23
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