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

被引：0
作者：
Hongmei Lin
Jianhong Shi
Tiejun Tong
Riquan Zhang
机构：
[1] Shanghai University of International Business and Economics,School of Statistics and Information
[2] Ministry of Education,Key Laboratory of Advanced Theory and Application in Statistics and Data Science
[3] East China Normal University,School of Mathematics and Computer Science
[4] Shanxi Normal University,Department of Mathematics
[5] Hong Kong Baptist University,School of Statistics
[6] East China Normal University,undefined
来源：
Journal of Systems Science and Complexity | 2022年 / 35卷
关键词：
Local linear regression; measurement error; partially linear model; SIMEX; single-index model;
D O I：
暂无
中图分类号：
学科分类号：
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.
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页码：2361 / 2380
页数：19
相关论文
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