Local linear estimation for the censored functional regression

被引：0

作者：

Almulhim, Fatimah A. ^{[1
]}

Merouan, Torkia ^{[2
]}

Alamari, Mohammed B. ^{[3
]}

Mechab, Boubaker ^{[2
]}

机构：

[1] Princess Nourah bint Abdulrahman Univ, Coll Sci, Dept Math Sci, POB 84428, Riyadh 11671, Saudi Arabia

[2] Univ Djillali Liabes Sidi Bel Abbes, Lab Stat & Stochast Proc, BP 89, Sidi Bel Abbes 22000, Algeria

[3] King Khalid Univ, Coll Sci, Dept Math, Abha 62529, Saudi Arabia

来源：

AIMS MATHEMATICS | 2024年 / 9卷 / 06期

关键词：

regression function; functional data; asymptotic normality; local linear estimation; Kaplan-Meier estimator; ASYMPTOTIC NORMALITY; NONPARAMETRIC REGRESSION; CONDITIONAL QUANTILE; MODELED REGRESSION; KERNEL ESTIMATOR; PREDICTION; DENSITY;

D O I：

10.3934/math.2024679

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

This work considers the Local Linear Estimation (LLE) of the conditional functional mean. This regression model is used when the independent variable is functional , and the dependent one is a censored scalar variable. Under standard postulates, we establish the asymptotic distribution of the LLE by proving its asymptotic normality. The obtained results show the superiority of the LLE approach over the functional local constant one. The feasibility of the studied model is demonstrated using artificial data. Finally, the usefulness of the obtained asymptotic distribution in incomplete functional data is highlighted through a real data application.

引用

页码：13980 / 13997

页数：18

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