In this paper we propose a semi-parametric, parsimonious value-at-risk forecasting model based on quantile regression and readily available market prices of option contracts from the over-the-counter ...

We develop a Bayesian method for nonparametric model—based quantile regression. The approach involves flexible Dirichlet process mixture models for the joint distribution of the response and the ...

Journal of Hydrometeorology, Vol. 17, No. 6 (June 2016), pp. 1869-1883 (15 pages) ABSTRACT Classical regression models are widely used in hydrological regional frequency analysis (RFA) in order to ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of neural network quantile regression. The goal of a quantile regression problem is to predict a single numeric ...

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 ...