Quantile regression models for current status data

Fang Shu Ou, Donglin Zeng, Jianwen Cai

Research output: Contribution to journalArticlepeer-review


Current status data arise frequently in demography, epidemiology, and econometrics where the exact failure time cannot be determined but is only known to have occurred before or after a known observation time. We propose a quantile regression model to analyze current status data, because it does not require distributional assumptions and the coefficients can be interpreted as direct regression effects on the distribution of failure time in the original time scale. Our model assumes that the conditional quantile of failure time is a linear function of covariates. We assume conditional independence between the failure time and observation time. An M-estimator is developed for parameter estimation which is computed using the concave–convex procedure and its confidence intervals are constructed using a subsampling method. Asymptotic properties for the estimator are derived and proven using modern empirical process theory. The small sample performance of the proposed method is demonstrated via simulation studies. Finally, we apply the proposed method to analyze data from the Mayo Clinic Study of Aging.

Original languageEnglish (US)
Pages (from-to)112-127
Number of pages16
JournalJournal of Statistical Planning and Inference
StatePublished - Nov 1 2016


  • Concave–convex procedure
  • Current status data
  • M-estimation
  • Quantile regression
  • Subsampling

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


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