Smoothing level selection for density estimators based on the moments

Rosa M. García-Fernández and Federico Palacios-González

Journal of Applied Statistics, 2024, vol. 51, issue 11, 2232-2257

Abstract: This paper introduces an approach to select the bandwidth or smoothing parameter in multiresolution (MR) density estimation and nonparametric density estimation. It is based on the evolution of the second, third and fourth central moments and the shape of the estimated densities for different bandwidths and resolution levels. The proposed method has been applied to density estimation by means of multiresolution densities as well as kernel density estimation (MRDE and KDE respectively). The results of the simulations and the empirical application demonstrate that the level of resolution resulting from the moments method performs better with multimodal densities than the Bayesian Information Criterion (BIC) for multiresolution densities estimation and the plug-in for kernel densities estimation.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:51:y:2024:i:11:p:2232-2257

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DOI: 10.1080/02664763.2023.2277125

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