Least squares | Robust statistics | Robust regression | Point estimation performance

Least absolute deviations

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 minimizing the sum of absolute deviations (sum of absolute residuals or sum of absolute errors) or the L1 norm of such values. It is analogous to the least squares technique, except that it is based on absolute values instead of squared values. It attempts to find a function which closely approximates a set of data by minimizing residuals between points generated by the function and corresponding data points. The LAD estimate also arises as the maximum likelihood estimate if the errors have a Laplace distribution. It was introduced in 1757 by Roger Joseph Boscovich. (Wikipedia).

Least absolute deviations
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From playlist Differentiation Application - Absolute Extrema

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From playlist Statistics: Describing Data

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From playlist 241Fall13Ex3

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From playlist Common Core Grade 7/8 Practice Standardized Test Math Problems

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From playlist Standard Deviation

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https://www.patreon.com/ProfessorLeonard Statistics Lecture 3.3: Finding the Standard Deviation of a Data Set

From playlist Statistics (Full Length Videos)

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From playlist MIT 5.310 Laboratory Chemistry, Fall 2019

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From playlist Level 1 Chartered Financial Analyst (CFA ®) Volume 1

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Related pages

Absolute value | Regression analysis | Errors and residuals | Median absolute deviation | Laplace distribution | Quantile regression | Square (algebra) | Least squares | Regularization (mathematics) | Maxima and minima | Function (mathematics) | Ordinary least squares | Quadratic function | Average absolute deviation | Lasso (statistics) | Robust regression | Simplex algorithm | Dependent and independent variables | Optimality criterion