Asymptotic properties of nonparametric quantile estimation with spatial dependency

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2022-11-10 DOI:10.1111/stan.12284

Serge-Hippolyte Arnaud Kanga, O. Hili, S. Dabo‐Niang, Assi N'Guessan

引用次数: 0

Abstract

The purpose of this work is to nonparametrically estimate the conditional quantile for a locally stationary multivariate spatial process. The new kernel quantile estimate derived from the one of conditional distribution function (CDF). The originality in the paper is based on the ability to take into account some local spatial dependency in estimate CDF form. Consistency and asymptotic normality of the estimates are obtained under α$$ \alpha $$ ‐mixing condition. Numerical study and application to real data are given in order to illustrate the performance of our methodology.

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具有空间相关性的非参数分位数估计的渐近性质

本文的目的是对局部平稳多元空间过程的条件分位数进行非参数估计。从条件分布函数(CDF)的核分位数估计出发，提出了新的核分位数估计。本文的独创性是基于在估计CDF形式中考虑一些局部空间依赖性的能力。在α $$ \alpha $$‐混合条件下，得到了估计的一致性和渐近正态性。通过数值研究和对实际数据的应用，说明了本文方法的有效性。

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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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

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