Asymptotic properties of nonparametric quantile estimation with spatial dependency

被引：0
|
作者：
Kanga, S. -H. Arnaud [1 ,4 ]
Hili, Ouagnina [1 ]
Dabo-Niang, Sophie [2 ,3 ]
N'Guessan, Assi [2 ]
机构：
[1] Inst Natl Polyteh Felix Houphouet Boigny, UMRI Math & Nouvelles Techcnol Informat, Yamousssoukro, Cote Ivoire
[2] Univ Lille, CNRS, UMR 8524 Lab Paul Painleve, Lille, France
[3] MODAL, INRIA, Lille, France
[4] Inst Natl Polyteh Felix Houphouet Boigny, UMRI Math & Nouvelles Techcnol Informat, 1093, Yamousssoukro, Cote Ivoire
来源：
STATISTICA NEERLANDICA | 2023年 / 77卷 / 03期
关键词：
almost complete convergence; asymptotic normality; local stationarity; random fields; strong mixing; KERNEL DENSITY-ESTIMATION; REGRESSION ESTIMATOR; PREDICTION;
D O I：
10.1111/stan.12284
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
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 & alpha;$$ \alpha $$-mixing condition. Numerical study and application to real data are given in order to illustrate the performance of our methodology.
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页码：254 / 283
页数：30
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