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 ...
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 ...
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 ...
The kernel_quantile_regression package is an open source implementation of the quantile regressor technique introduced in [1]. This repo contains the code for reproducing the research paper ...
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 ...
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 ...
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