Partially Linear Single-Index Model in the Presence of Measurement Error

被引：0

作者：

LIN Hongmei ^{[1
,2
]}

SHI Jianhong ^{[3
]}

TONG Tiejun ^{[4
]}

ZHANG Riquan ^{[5
]}

机构：

[1] School of Statistics and Information, Shanghai University of International Business and Economics

[2] Key Laboratory of Advanced Theory and Application in Statistics and Data Science, Ministry of Education, East China Normal University

[3] School of Mathematics and Computer Science, Shanxi Normal University

[4] Department of Mathematics, Hong Kong Baptist University

[5] School of Statistics, East China Normal University

来源：

Journal of Systems Science & Complexity | 2022年 / 35卷 / 06期

基金：

中国国家自然科学基金;

关键词：

D O I：

暂无

中图分类号：

O212.1 [一般数理统计];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

The partially linear single-index model(PLSIM) is a flexible and powerful model for analyzing the relationship between the response and the multivariate covariates. This paper considers the PLSIM with measurement error possibly in all the variables. The authors propose a new efficient estimation procedure based on the local linear smoothing and the simulation-extrapolation method,and further establish the asymptotic normality of the proposed estimators for both the index parameter and nonparametric link function. The authors also carry out extensive Monte Carlo simulation studies to evaluate the finite sample performance of the new method, and apply it to analyze the osteoporosis prevention data.

引用

页码：2361 / 2380

页数：20

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[1] Partially Linear Single-Index Model in the Presence of Measurement Error
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Shi, Jianhong
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[J]. JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY, 2022, 35 (06) : 2361 - 2380
[2] Partially Linear Single-Index Model in the Presence of Measurement Error
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