We study censored quantile regression with covariates measured with errors. We propose a composite quantile objective function based on inverse censoring-probability weighting, and an averaging ...

Quantile regression has emerged as a significant extension of traditional linear models and its potential in survival applications has recently been recognized. In this paper we study quantile ...

The goal of a machine learning regression problem is to predict a single numeric value. Quantile regression is a variation where you are concerned with under-prediction or over-prediction. I'll phrase ...

One of the more difficult challenges for modeling is deciding how (or if) to deal with extreme data points. It’s a common problem in economic and financial numbers. Fat tailed distributions are ...

Bayesian quantile regression and statistical modelling represent a growing paradigm in contemporary data analysis, extending conventional regression by estimating various conditional quantiles rather ...

Immunotherapy has been approved to treat many tumor types. However, one characteristic of this therapeutic class is that survival benefit is due to late immune response, which leads to a delayed ...