De Backer M, El Ghouch A, Van Keilegom I (2019) An adapted loss function for censored quantile regression. J Am Stat Assoc 114(527):1126–1137 Article MathSciNet MATH Google Scholar Delgado MA, García-Suaza A, Sant’Anna PH (2022) Distribution regression in duration analysis: an appl...
2. Quantile Regression的实现方式： 神经网络模型：通过神经网络模型表示分布的累积分布函数。 1Wasserstein距离投影：使用1Wasserstein距离进行投影，以确保学习的分布与真实分布之间的距离最小化。 损失函数设计：Quantile Regression的损失函数旨在精确估计分位数，通过平滑的Quantile Huber loss解决导数不连续的...
We do not assume any prior knowledge of the group structure and combine the quantile regression loss function with the recently proposed convex clustering penalty of Hocking et al. (2011). The convex clustering method introduces a ℓ1-constraint on the pair-wise difference of the individual fixe...
quantile regressioncontinuous ranked probability scorequantile loss functioncheck functionWind power forecasting techniques have received substantial attention recently due to the increasing penetration of wind energy in national power systems. While the initial focus has been on point forecasts, the need to...
For instance, quantile regression relies on minimizing the conditional quantile loss, which is based on the quantile check function (Koenker and Bassett Jr 1978). This has been extended to more flexible regression functions such as the quantile regression forest (Meinshausen 2006) and the gradient ...
is whatever objective function we’re using, which in our cases is RegressionQuantileloss This is the implementation of obj->RenewTreeOutput() for quantile regression this just calls the PercentileFun() function on each leaf in order to get the αα-quantile of the data in that leafThis...
Mean regression is a common application of high-dimensional inference and one setting in which Gibbs posteriors have already been studied; see, for example, Syring and Martin (2022). Let (X, Y ) ∼ P and consider the loss function ℓθ(x, y) = {y−θ(x)}2, for θ a generic...
Learn how to use the Fast Forest Quantile Regression component to create a regression model that can predict values for a specified number of quantiles.
Dataset D was used for training several QoT models, with each QoT model minimizing a different loss function. Specifically, for quantile regression, the loss function in Eq. (1) was applied for q=0.05, q=0.1, q=0.15, q=0.2, and q=0.25 to create the lower QoT estimates, denoted ...
Learn how to use the Fast Forest Quantile Regression component to create a regression model that can predict values for a specified number of quantiles.