Compared with commonly-used point forecasting, probabilistic forecasting provides quantitative information on the uncertainty associated with wind power output. However, most studies focus on ...

We provide an algorithm to build quantile regression trees in O(N log N) time, where N is the number of instances in the training set. Quantile regression trees are regression trees that model ...

In this paper we study doubly robust estimators of various average and quantile treatment effects under unconfoundedness; we also consider an application to a setting with an instrumental variable. We ...

Among those approaches, Conditional Quantile Regression (ConQuR) (Ling et al., 2021a, b) utilizes a conditional quantile regression approach for batch effect correction, enabling the generation of ...

It then gives tables to five places of values of the six trigonometric functions mentioned, as well as a table of decimal equivalents. In addition to this material, the pamphlet has a section on ...

Working with the Quantile () function resulted in some confusion (which might be based on a conceptual misunderstanding): If the sum of weights is 1, the function (type = 7, i.e., default) returns the ...

By allowing conditional functions to be specified at any point across the selected quantiles, quantile regression helps describe the whole conditional distribution of the responsive variables with ...