Inequality for the variance of an asymmetric loss

12/31/2022

∙

We assume that the forecast error follows a probability distribution which is symmetric and monotonically non-increasing on non-negative real numbers, and if there is a mismatch between observed and predicted value, then we suffer a loss. Under the assumptions, we solve a minimization problem with an asymmetric loss function. In addition, we give an inequality for the variance of the loss.

READ FULL TEXT

Inequality for the variance of an asymmetric loss

Minimizing the expected value of the asymmetric loss and an inequality of the variance of the loss

Bayes-optimal Hierarchical Classification over Asymmetric Tree-Distance Loss

Classification in asymmetric spaces via sample compression

The risk function of the goodness-of-fit tests for tail models

A bayesian wavelet shrinkage rule under LINEX loss function

PACMAN: PAC-style bounds accounting for the Mismatch between Accuracy and Negative log-loss

Efficiently Approximating the Probability of Deadline Misses in Real-Time Systems

Inequality for the variance of an asymmetric loss

Related Research

Minimizing the expected value of the asymmetric loss and an inequality of the variance of the loss

Bayes-optimal Hierarchical Classification over Asymmetric Tree-Distance Loss

Classification in asymmetric spaces via sample compression

The risk function of the goodness-of-fit tests for tail models

A bayesian wavelet shrinkage rule under LINEX loss function

PACMAN: PAC-style bounds accounting for the Mismatch between Accuracy and Negative log-loss

Efficiently Approximating the Probability of Deadline Misses in Real-Time Systems