Algorithms for unconstrained \(L_ 1\) simple linear regression

DOI10.1016/0167-9473(88)90073-4zbMath0726.62102OpenAlexW2026159234MaRDI QIDQ804173

N. E. Zubov

Publication date: 1988

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0167-9473(88)90073-4

zbMATH Keywords

algorithmscomputer programsL(1)-norm

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Probabilistic methods, stochastic differential equations (65C99)

Related Items

Quantile-Based Iterative Methods for Corrupted Systems of Linear Equations, Fitting rectilinear polgonal curves to a set of points in the plane., Least absolute value regression: recent contributions, Applied regression analysis bibliography update 1988-89

Uses Software

• AS 74
• AS 132
• Algorithm 478
• Algorithm 564

Cites Work

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• Algorithms for best \(L_ 1\) and \(L_ \infty\) linear approximations on a discrete set
• Optimal Estimation of Executive Compensation by Linear Programming
• Linear Curve Fitting Using Least Deviations
• Linear Programming Techniques for Regression Analysis
• Computational Algorithms for Calculating Least Absolute Value and Chebyshev Estimates for Multiple Regression
• Least absolute value estimation in regression models: an annotated bibliography
• Algorithm AS 132: Least Absolute Value Estimates for a Simple Linear Regression Problem
• A revised simplex algorithm for the absolute deviation curve fitting problem
• A Test Problem Generator for Discrete Linear L ₁ Approximation Problems
• L ₁ solution of overdetermined systems of linear equations
• A new descent algorithm for the least absolute value regression problem
• Least Absolute Deviations Curve-Fitting
• A Descent Method for the Uniform Solution to Over-Determined Systems of Linear Equations
• On least absolute values estimation
• Minimization Techniques for Piecewise Differentiable Functions: The $l_1$ Solution to an Overdetermined Linear System
• An Improved Algorithm for Discrete $l_1 $ Linear Approximation
• A Method for Minimizing the Sum of Absolute Values of Deviations