Parametric modeling of quantile regression coefficient functions with censored and truncated data.
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.
Karolinska Institutet, Institute of Environmental Medicine, Unit of Biostatistics Nobels väg 13, 17177 Stockholm, Sweden.
Paolo
P
Frumento
Karolinska Institutet, Institute of Environmental Medicine, Unit of Biostatistics Nobels väg 13, 17177 Stockholm, Sweden.
Matteo
M
Bottai
D003198:Computer Simulation; D006801:Humans; D015233:Models, Statistical; D012044:Regression Analysis
Censored and truncated quantile regression; Conditional quantile function; Quantile regression coefficients modeling; R package qrcm