Nonparametric kernel estimation of conditional copula density

Pub Date : 2024-05-11 DOI:10.1016/j.spl.2024.110154
Toihir Soulaimana Djaloud, Cheikh Tidiane Seck
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Abstract

This paper introduces a new mathematical formula for the bivariate conditional copula density and proposes kernel-type estimators for it. We demonstrate the consistency and asymptotic normality of these estimators, which also exhibit the best quadratic mean convergence rate when the optimal theoretical bandwidth is selected.

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条件共轭密度的非参数核估计
本文介绍了一种新的双变量条件 copula 密度数学公式,并提出了核型估计器。我们证明了这些估计器的一致性和渐近正态性,当选择最佳理论带宽时,它们还表现出最佳的二次均值收敛率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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