Smoothed and Corrected Score Approach to Censored Quantile Regression With Measurement Errors

Yuanshan Wu, Yanyuan Ma, Guosheng Yin

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

Censored quantile regression is an important alternative to the Cox proportional hazards model in survival analysis. In contrast to the usual central covariate effects, quantile regression can effectively characterize the covariate effects at different quantiles of the survival time. When covariates are measured with errors, it is known that naively treating mismeasured covariates as error-free would result in estimation bias. Under censored quantile regression, we propose smoothed and corrected estimating equations to obtain consistent estimators. We establish consistency and asymptotic normality for the proposed estimators of quantile regression coefficients. Compared with the naive estimator, the proposed method can eliminate the estimation bias under various measurement error distributions and model error distributions. We conduct simulation studies to examine the finite-sample properties of the new method and apply it to a lung cancer study. Supplementary materials for this article are available online.

Original languageEnglish (US)
Pages (from-to)1670-1683
Number of pages14
JournalJournal of the American Statistical Association
Volume110
Issue number512
DOIs
StatePublished - Oct 2 2015

Fingerprint

Corrected Score
Censored Regression
Quantile Regression
Measurement Error
Covariates
Estimator
Cox Proportional Hazards Model
Model Error
Lung Cancer
Estimating Equation
Survival Analysis
Consistent Estimator
Survival Time
Regression Coefficient
Quantile
Asymptotic Normality
Eliminate
Simulation Study
Measurement error
Quantile regression

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

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Smoothed and Corrected Score Approach to Censored Quantile Regression With Measurement Errors. / Wu, Yuanshan; Ma, Yanyuan; Yin, Guosheng.

In: Journal of the American Statistical Association, Vol. 110, No. 512, 02.10.2015, p. 1670-1683.

Research output: Contribution to journalArticle

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