Estimating the conditional quantiles of outcome variables of interest is frequent in many research areas, and quantile regression is foremost among the utilized methods. The coefficients of a quantile ...

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 ...

This work proposes new inference methods for a regression coefficient of interest in a (heterogenous) quantile regression model. We consider a high-dimensional model where the number of regressors ...

Quantile regression techniques were used to estimate the influence of employment and hours worked on percentage weight change over 2 years across the entire distribution of weight change in a cohort ...

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 ...

This paper investigates the drivers of reserves in emerging markets (EMs) and small island (SIs) and develops an operational metric for estimating reserves in SIs taking into account their unique ...