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 ...

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

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 ...

Covariates may affect continuous responses differently at various points of the response distribution. For example, some exposure might have minimal impact on conditional means, whereas it might lower ...

Abstract: Additive quantile regression provides a flexible and robust approach for modeling nonlinear relationships. The usual method of fitting an additive quantile model is to approximate the ...

Abstract: Electricity load forecasting is an essential part of power system planning and operation, and it is crucial to make accurate predictions. The smart grid paradigm and the new energy market ...

Air-quality analysis of scrap-tire animal singeing sites in Ghana—PM2.5/PM10/CO/SO₂/VOCs—using LME, GAM, and quantile models with WHO guideline checks.

When I generate the residual plots using the DHARMa package, the quantile regression curves do not appear in the residual vs. predicted plot (right plot). Instead, I receive warnings stating that the ...