Truncated composite quantile regression with covariates measurement errors

Truncated data and measurem ent error data are often encountered in practice. In this paper, we study two classes of truncated composite quantile regression with covariates measured with errors. We propose weighted composite quantile regression estimators of the slope parameters based on weights whi...

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Bibliographic Details
Published inCommunications in statistics. Simulation and computation Vol. 54; no. 7; pp. 2599 - 2616
Main Authors Xu, Hongxia, Qin, Mengting, Fan, Guoliang
Format Journal Article
LanguageEnglish
Published Taylor & Francis 03.07.2025
Subjects
Asymptotic normality
Bias correction
Composite quantile regression
Measurement error
Randomly truncated data
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Summary:Truncated data and measurem ent error data are often encountered in practice. In this paper, we study two classes of truncated composite quantile regression with covariates measured with errors. We propose weighted composite quantile regression estimators of the slope parameters based on weights which are random quantities and determined by the product-limit estimates of the distribution function of truncated variable. However, the estimators are often biased when one treats mismeasured covariates as error-free. To eliminate the bias and obtain consistent estimators, a debiasing technique is proposed. The resulting estimates are proved to be asymptotically normal and root n consistent under some mild conditions. Simulation studies and a real data example show that the proposed methods work promisingly.
ISSN:0361-0918
1532-4141
DOI:10.1080/03610918.2024.2314672