Quantile regression for interval censored data

ABSTRACT

As direct generalization of the quantile regression for complete observed data, an estimation method for quantile regression models with interval censored data is proposed, and the property of consistency is obtained. The property of asymptotic normality is also established with a bias converging to zero, and to reduce the bias, two bias correction methods are proposed. Methods proposed in this paper do not require the censoring vectors to be identically distributed, and can be applied to models with various covariates. Simulation results show that the proposed methods work well.

KEYWORDS:

• Asymptotic normality
• bias correction
• interval censored data
• quantile regression.

Mathematics Subject Classification:

• 62N02
• 62J05
• 62F12

Funding

The research is supported by NSFC (No. 11201235; 11471252) and Program of Natural Science Research of Jiangsu Higher Education Institutions of China(No. 12KJB110010).