Partial index additive models with additive distortion measurement errors

被引：1

作者：

Zhang, Jun ^{[1
]}

Feng, Sanying ^{[2
]}

Gai, Yujie ^{[3
]}

机构：

[1] Shenzhen Univ, Coll Math & Stat, Shenzhen, Peoples R China

[2] Zhengzhou Univ, Sch Math & Stat, Zhengzhou, Peoples R China

[3] Cent Univ Finance & Econ, Sch Math & Stat, Beijing 100081, Peoples R China

来源：

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION | 2022年 / 51卷 / 05期

基金：

中国国家自然科学基金;

关键词：

Additive distortion; Local linear smoothing; Partial linear models; Single-index; PARTIAL LINEAR-MODELS; STATISTICAL-INFERENCE; DIMENSION REDUCTION; VARIABLE SELECTION; REGRESSION MODELS;

D O I：

10.1080/03610918.2020.1757712

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：

This paper considers the estimation for a partial index additive regression model, when the response variable and covariates in the index part are observed with additive distortion measurement errors. For the index parameter, the dimension-reduction based estimators with or without additive distortion measurement errors are proposed. This new estimation method is further adopted to the partial linear models for parameter estimation. We study the asymptotic properties of the proposed estimators. Simulation studies are conducted to compare the proposed estimation methods.

引用

页码：2191 / 2216

页数：26

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