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

## TL;DR

This paper proposes a method to adjust predictions to minimize the expected asymmetric loss and variance of the loss, without estimating regression coefficients, by ensuring the prediction error follows a normal distribution.

## Contribution

It introduces a novel approach that corrects predictions to optimize asymmetric loss and variance without traditional coefficient estimation.

## Key findings

- Effective reduction in expected asymmetric loss.
- Lowered variance of prediction errors.
- Applicable to various prediction scenarios.

## Abstract

For some estimations and predictions, we solve minimization problems with asymmetric loss functions. Usually, we estimate the coefficient of regression for these problems. In this paper, we do not make such the estimation, but rather give a solution by correcting any predictions so that the prediction error follows a general normal distribution. In our method, we can not only minimize the expected value of the asymmetric loss, but also lower the variance of the loss.