Minimizing the expected value of the asymmetric loss function and an inequality for the variance of the loss
Minimizing the expected value of the asymmetric loss function and an inequality for the variance of the loss
复制标题
最小化不对称损失函数的期望值和损失方差的不等式
DOI：
10.1080/02664763.2020.1761951
复制
发表时间：
2020
影响因子：
1.5
通讯作者：
Y. Yamaguchi and R. Nishii
中科院分区：
文献类型：
--
作者：
N. Yamaguchi;Y. Yamaguchi and R. Nishii
通常针对具有不对称损失函数的最小化问题来估计回归系数。在本文中，我们宁愿纠正预测，使预测误差遵循广义高斯分布。在我们的方法中，我们不仅最小化不对称损失的期望值，而且还降低损失的方差。预测通常都会有错误。因此，有必要在考虑这些误差的情况下进行预测。我们的方法考虑了预测误差。此外，即使我们不理解预测方法，这也是一种可能的情况，例如深度学习中，如果我们知道预测误差分布和不对称损失函数，我们就可以使用我们的方法。我们的方法可以应用于从电力市场采购电力。
The coefficients of regression are usually estimated for minimization problems with asymmetric loss functions. In this paper, we rather correct predictions so that the prediction error follows a generalized Gaussian distribution. In our method, we not only minimize the expected value of the asymmetric loss, but also lower the variance of the loss. Predictions usually have errors. Therefore, it is necessary to use predictions in consideration of these errors. Our approach takes into account prediction errors. Furthermore, even if we do not understand the prediction method, which is a possible circumstance in, e.g. deep learning, we can use our method if we know the prediction error distribution and asymmetric loss function. Our method can be applied to procurement of electricity from electricity markets.