Asymptotic normality of kernel functional regression estimator based upon twice censored data

被引：0

作者：

Sarra, Leulmi ^{[1
]}

机构：

[1] Univ Freres Mentouri Constantine 1, Dept Math, Lab LAMASD, Constantine, Algeria

来源：

JOURNAL OF THE KOREAN STATISTICAL SOCIETY | 2025年 / 54卷 / 01期

关键词：

Asymptotic normality; Functional data; Kernel estimation; Regression function; Twice censored data; CONVERGENCE; CONSISTENCY; DENSITY;

D O I：

10.1007/s42952-024-00293-0

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：

In this paper, we introduce a new kernel functional regression estimator when the random response variable is subject to twice censoring. Then, we establish the asymptotic normality of our estimator and we deduce the asymptotic confidence interval of the regression function. To enhance our theoretical results, the performance and the asymptotic Gaussian behavior of our estimator are highlighted through a simulation study and an application to real data.

引用

页码：220 / 247

页数：28

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