Introduction: The scientific measurement of urban atmospheric environmental efficiency is a vital prerequisite for achieving air pollution control and regional green high-quality development. Methods: ...

As one of the important statistical methods, quantile regression (QR) extends traditional regression analysis. In QR, various quantiles of the response variable are modeled as linear functions of the ...

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 ...

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 ...

We investigate nonparametric regression methods based on spatial depth and quantiles when the response and the covariate are both functions. As in classical quantile regression for finite dimensional ...

Abstract: This paper introduces a novel Poisson-unit-Weibull (PUW) distribution, which is defined on a unit domain and characterized by three parameters. The PUW distribution is capable of ...

Add a description, image, and links to the quantile-regression topic page so that developers can more easily learn about it.

This study evaluated the efficiency of genome-wide selection (GWS) based on regularized quantile regression (RQR) to obtain genomic growth curves based on genomic estimated breeding values (GEBV) of ...

In many simulation-based Bayesian approaches to quantile regression, Markov Chain Monte Carlo techniques are employed to generate draws from a posterior distribution based on an asymmetric Laplace ...