ASYMPTOTIC NORMALITY SINGLE FUNCTIONAL INDEX QUANTILE REGRESSION UNDER RANDOMLY CENSORED DATA

被引：0

作者：

Dib, Abdessamad ^{[1
]}

Hamri, Mohamed Mehdi ^{[2
]}

Rabhi, Abbes ^{[3
]}

机构：

[1] Univ Djillali Liabes Sidi Bel Abbes, Lab Math, Sidi Bel Abbes 22000, Algeria

[2] Univ Djillali Liabes Sidi Bel Abbes, ESI, LABRI, Sidi Bel Abbes 22000, Algeria

[3] Univ Djillali Liabes Sidi Bel Abbes, Sidi Bel Abbes 22000, Algeria

来源：

JOURNAL OF SCIENCE AND ARTS | 2022年 / 04期

关键词：

Asymptotic normality; conditional quantile; functional single-index process; functional random variable; nonparametric estimation; small ball probability; ESTIMATOR;

D O I：

10.46939/J.Sci.Arts-22.4-a07

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

The main objective of this paper is to estimate non-parametrically the quantiles of a conditional distribution based on the single-index model in the censorship model when the sample is considered as an independent and identically distributed (i.i.d.) random variables. First of all, a kernel type estimator for the conditional cumulative distribution function (cond-cdf) is introduced. Afterwards, we give an estimation of the quantiles by inverting this estimated cond-cdf, the asymptotic properties are stated when the observations are linked with a single-index structure. Finally, a simulation study is carried out to evaluate the performance of this estimate.

引用

页码：845 / 864

页数：20

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