Least absolute value and chebychev estimation utilizing least squares results

DOI10.1007/BF01585115zbMath0536.65009MaRDI QIDQ3321322

Michael G. Sklar, Ronald D. Armstrong

Publication date: 1982

Published in: Mathematical Programming (Search for Journal in Brave)

zbMATH Keywords

minimaxcurve fittingleast squaresexploratory data analysisChebyshev normleast absolute values

Mathematics Subject Classification ID

Numerical smoothing, curve fitting (65D10) Linear regression; mixed models (62J05) Probabilistic methods, stochastic differential equations (65C99)

Related Items (4)

Estimating the parameters in regression with uniformly distributed errors ⋮ Non-asymptotic bounds for the \(\ell_{\infty}\) estimator in linear regression with uniform noise ⋮ Lagrangian approach for large-scale least absolute value estimation ⋮ Extensions to a Best Subset Algorithm for Least Absolute Value Estimation

Uses Software

• IMSL Numerical Libraries
• AS 135

Cites Work

• Algorithms for best \(L_ 1\) and \(L_ \infty\) linear approximations on a discrete set
• On L1 and Chebyshev estimation
• A revised simplex algorithm for the absolute deviation curve fitting problem
• Algorithm AS 135: Min-Max Estimates for a Linear Multiple Regression Problem
• Using the least squares estimator in Chebyshev estimation
• Algorithm 495: Solution of an Overdetermined System of Linear Equations in the Chebychev Norm [F4]
• Using the $L_2 $-Estimator in $L_1 $-Estimation
• A Comparison of Two Algorithms for Absolute Deviation Curve Fitting
• Discrete approximation in the L1 norm
• An Improved Algorithm for Discrete $l_1 $ Linear Approximation
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