Parametric modeling of quantile regression coefficient functions with censored and truncated data

Paolo Frumento and Matteo Bottai

Biometrics, 2017, vol. 73, issue 4, 1179-1188

Abstract: Quantile regression coefficient functions describe how the coefficients of a quantile regression model depend on the order of the quantile. A method for parametric modeling of quantile regression coefficient functions was discussed in a recent article. The aim of the present work is to extend the existing framework to censored and truncated data. We propose an estimator and derive its asymptotic properties. We discuss goodness‐of‐fit measures, present simulation results, and analyze the data that motivated this article. The described estimator has been implemented in the R package qrcm.

Date: 2017
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