DGQR estimation for interval censored quantile regression with varying-coefficient models

PLoS One. 2020 Nov 10;15(11):e0240046. doi: 10.1371/journal.pone.0240046. eCollection 2020.

Abstract

This paper propose a direct generalization quantile regression estimation method (DGQR estimation) for quantile regression with varying-coefficient models with interval censored data, which is a direct generalization for complete observed data. The consistency and asymptotic normality properties of the estimators are obtained. The proposed method has the advantage that does not require the censoring vectors to be identically distributed. The effectiveness of the method is verified by some simulation studies and a real data example.

MeSH terms

  • Computer Simulation
  • Data Interpretation, Statistical
  • Models, Statistical*
  • Regression Analysis

Grants and funding

The author(s) received no specific funding for this work.