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

Hongmei Lin*, Jianhong Shi*, Tiejun Tong*, Riquan Zhang*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review


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.

Original languageEnglish
Pages (from-to)2361-2380
Number of pages20
JournalJournal of Systems Science and Complexity
Issue number6
Early online date3 Aug 2022
Publication statusPublished - Dec 2022

Scopus Subject Areas

  • Computer Science (miscellaneous)
  • Information Systems

User-Defined Keywords

  • Local linear regression
  • measurement error
  • partially linear model
  • single-index model


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