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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.
MAE is calculated as the sum of absolute errors (i.e., the Manhattan distance) divided by the sample size: [1] = = | | = = | |. It is thus an arithmetic average of the absolute errors | e i | = | y i − x i | {\displaystyle |e_{i}|=|y_{i}-x_{i}|} , where y i {\displaystyle y_{i}} is the prediction and x i {\displaystyle x_{i}} the true value.
Best rational approximants for π (green circle), e (blue diamond), ϕ (pink oblong), (√3)/2 (grey hexagon), 1/√2 (red octagon) and 1/√3 (orange triangle) calculated from their continued fraction expansions, plotted as slopes y/x with errors from their true values (black dashes)
Most commonly the absolute percent errors are weighted by the actuals (e.g. in case of sales forecasting, errors are weighted by sales volume). [3] Effectively, this overcomes the 'infinite error' issue. [ 4 ]
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The article about Least absolute deviations (LAD), in the section "Solving methods", omits a simple transformation that casts LAD problems as Linear Programs (LP), which can in turn be reliably and efficiently solved by general purpose LP packages (for the transformation, see p. 294 of Boyd and Vandenberghe's book "Convex Optimization", freely ...
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