非平稳随机的非参数条件分位数估计

Afrika Statistika Pub Date : 2021-10-01 DOI:10.16929/as/2021.3009.193

Serge-Hippolyte Arnaud Kanga, O. Hili, S. Dabo‐Niang

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引用次数: 1

摘要

提出了一种实值非平稳空间过程的核条件分位数估计方法，用于底层过程的非观测位置的预测目标。独创性是基于考虑一些局部空间依赖性的能力。建立了基于估计量的几乎完全一致性和\(L^q\) -一致性的大样本性质。为了说明我们的方法的性能，给出了一个数值研究。

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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On Nonparametric Conditional Quantile Estimation for Non-stationary Random

A kernel conditional quantile estimate of a real-valued non-stationary spatial process is proposed for a prediction goal at a non-observed location of the underlying process. The originality is based on the ability to take into account some local spatial dependency. Large sample properties based on almost complete and \(L^q\)-consistencies of the estimator are established. A numerical study is given in order to illustrate the performance of our methodology.

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Afrika Statistika

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