Local Linear Estimator of the Conditional Hazard Function For Index Model in Case of Missing at Random Data

被引：0

作者：

Abdelhak, Khadidja ^{[2
]}

Belguerna, Abderrahmane ^{[1
]}

Laala, Zeyneb ^{[1
]}

机构：

[1] Salhi Ahmed Univ Ctr, Dept Math & Comp Sci, BP 66, Naama 45000, Algeria

[2] Univ Djillali Liabes Sidi Bel Abbes, Lab Anal & Control PDE, Sidi Bel Abbes, Algeria

来源：

APPLICATIONS AND APPLIED MATHEMATICS-AN INTERNATIONAL JOURNAL | 2022年 / 17卷 / 01期

关键词：

Almost complete convergence; Conditional hazard function; Functional data; Index model; Local linear; Missing at random; Rate; NONPARAMETRIC-ESTIMATION; ASYMPTOTIC NORMALITY; REGRESSION;

D O I：

暂无

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

The estimation of hazard function becomes an important tool in statistics. Also, the single-index model is an essential method that has much application in a lot of domains but the use of this model in nonparametric estimation is very limited, and only a few theoretical results are available in the literature. In this paper, a kernel estimator of the conditional hazard function is proposed for the index model using the local linear model. This estimator is created under some regularity conditions and in the case of missing data at random. Then, the almost complete convergence is established with rate for the proposed estimator. The results prove that it has good asymptotic properties.

引用

页码：33 / 53

页数：22

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