Censored quantile regression with covariate measurement errors

Yanyuan Ma, Guosheng Yin

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

We study censored quantile regression with covariates measured with errors. We propose a composite quantile objective function based on inverse censoringprobability weighting, and an averaging estimator to improve estimation efficiency. Our procedure can eliminate the bias in the naive estimator that is obtained by treating mismeasured covariates as error-free. Using a combination of martingale and quantile regression techniques, we show that the proposed estimators for the regression coefficients are consistent and asymptotically normal. We conducted simulation studies to examine the finite-sample properties of the new method, and demonstrated efficiency gain of the averaging estimator over the single quantile regression estimator. For illustration, we applied our model to a lung cancer study.

Original languageEnglish (US)
Pages (from-to)949-971
Number of pages23
JournalStatistica Sinica
Volume21
Issue number2
DOIs
StatePublished - Jan 1 2011

Fingerprint

Censored Regression
Quantile Regression
Measurement Error
Covariates
Estimator
Averaging
Quantile Function
Regression Estimator
Lung Cancer
Regression Coefficient
Martingale
Weighting
Eliminate
Objective function
Composite
Simulation Study
Measurement error
Quantile regression

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

Ma, Yanyuan ; Yin, Guosheng. / Censored quantile regression with covariate measurement errors. In: Statistica Sinica. 2011 ; Vol. 21, No. 2. pp. 949-971.
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Censored quantile regression with covariate measurement errors. / Ma, Yanyuan; Yin, Guosheng.

In: Statistica Sinica, Vol. 21, No. 2, 01.01.2011, p. 949-971.

Research output: Contribution to journalArticle

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