A piecewise linear approximation procedure forLpnorm curve fitting

DOI10.1080/00949659508811683zbMath0839.65011OpenAlexW2072095282MaRDI QIDQ4869592

Michael G. Sklar, Ronald D. Armstrong

Publication date: 26 March 1996

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949659508811683

zbMATH Keywords

separable programmingcurve fittingregressionpiecewise linear approximationmulti-vertex pivotsunconstrained nonlinear convex minimization

Mathematics Subject Classification ID

Numerical smoothing, curve fitting (65D10) Linear regression; mixed models (62J05) Numerical mathematical programming methods (65K05) Convex programming (90C25) Probabilistic methods, stochastic differential equations (65C99)

Cites Work

• Unnamed Item
• Goal programming and multiple objective optimizations. Part I
• Two-Segment Separable Programming
• Piecewise-Linear Approximation Methods for Nonseparable Convex Optimization
• A revised simplex algorithm for the absolute deviation curve fitting problem
• Algorithm AS 135: Min-Max Estimates for a Linear Multiple Regression Problem
• The linear regression model: L_pnorm estimation and the choice of p
• Explicit Solutions in Convex Goal Programming
• Nonuniqueness of least absolute values regression
• On least absolute values estimation
• $L_p $-Criteria for the Estimation of Location Parameters
• Robust Estimation of Straight Line Regression Coefficients by Minimizing pth Power Deviations
• Norms for Smoothing and Estimation

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