MAE stands for mean absolute error, which is a measure of how close your predictions are to the actual values in a regression problem. It is calculated by taking the average of the absolute ...

In this paper, we exploit the properties of mean absolute error (MAE) as a loss function for the deep neural network (DNN) based vector-to-vector regression. Th ...

Multiplicative regression model or accelerated failure time model, which becomes linear regression model after logarithmic transformation, is useful in analyzing data with positive responses, such as ...

Steven P. Ellis, Instability of Least Squares, Least Absolute Deviation and Least Median of Squares Linear Regression, Statistical Science, Vol. 13, No. 4 (Nov., 1998 ...