STRONG UNIFORM CONSISTENCY RATES OF CONDITIONAL DENSITY ESTIMATION IN THE SINGLE FUNCTIONAL INDEX MODEL FOR FUNCTIONAL DATA UNDER RANDOM CENSORSHIP

被引：0

作者：

Kadiri, Nadia ^{[1
]}

Meghnafi, Mustapha ^{[2
]}

Rabhi, Abbes ^{[3
]}

机构：

[1] Univ Djillali LIABES Sidi Bel Abbes, Sidi Bel Abbes, Algeria

[2] Univ Bechar, Fac Exact Sci, Dept Math, Bechar, Algeria

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

来源：

REVSTAT-STATISTICAL JOURNAL | 2022年 / 20卷 / 02期

关键词：

conditional density; functional single-index process; functional random variable; nonparametric estimation; small ball probability; NONPARAMETRIC-ESTIMATION; ASYMPTOTIC NORMALITY; LAWS;

D O I：

暂无

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 main objective of this paper is to investigate the estimation of conditional density function based on the single-index model in the censorship model when the sample is considered as an inde-pendent and identically distributed (i.i.d.) random variables. First of all, a kernel type estimator for the conditional density function (cond-df) is introduced. Afterwards, the asymptotic properties are stated when the observations are linked with a single-index structure. The pointwise almost com-plete convergence and the uniform almost complete convergence (with rate) of the kernel estimate of this model are established. As an application the conditional mode in functional single-index model is presented. Finally, a simulation study is carried out to evaluate the performance of this estimate.

引用

页码：221 / 249

页数：29

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