Least absolute deviations (LAD), also known as Least Absolute Errors (LAE), Least Absolute Value (LAV), or the L1 norm problem, is a mathematical optimization technique similar to the popular least squares technique that attempts to find a function which closely approximates a set of data. In the simple case of a set of (x,y) data, the approximation function is a simple "trend line" in two-dimensional Cartesian coordinates. The method minimizes the sum of absolute errors (SAE) (the sum of the absolute values of the vertical "residuals" between points generated by the function and corresponding points in the data). The least absolute deviations estimate also arises as the maximum likelihood estimate if the errors have a Laplace distribution.
Read more about Least Absolute Deviations: Formulation of The Problem, Contrasting Least Squares With Least Absolute Deviations, Other Properties, Variations, Extensions, Specializations, Solving Methods, See Also
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“Point me out the happy man and I will point you out either egotism, selfishness, evil—or else an absolute ignorance.”
—Graham Greene (1904–1991)