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The absolute difference between A t and F t is divided by half the sum of absolute values of the actual value A t and the forecast value F t. The value of this calculation is summed for every fitted point t and divided again by the number of fitted points n.
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This property, undesirable in many applications, has led researchers to use alternatives such as the mean absolute error, or those based on the median. See also. Bias–variance tradeoff; Hodges' estimator; James–Stein estimator; Mean percentage error; Mean square quantization error; Mean square weighted deviation; Mean squared displacement
The average of all the sample absolute deviations about the mean of size 3 that can be drawn from the population is 44/81, while the average of all the sample absolute deviations about the median is 4/9. Therefore, the absolute deviation is a biased estimator. However, this argument is based on the notion of mean-unbiasedness.
Least absolute deviations (LAD), also known as least absolute errors (LAE), least absolute residuals (LAR), or least absolute values (LAV), is a statistical optimality criterion and a statistical optimization technique based on minimizing the sum of absolute deviations (also sum of absolute residuals or sum of absolute errors) or the L 1 norm of such values.