Sensitivity analysis for predictor variables in the MSAE regression.

DOI10.1016/S0167-9473(01)00110-4zbMath1098.62544MaRDI QIDQ1608910

Subhash C. Narula, John F. Wellington

Publication date: 13 August 2002

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

zbMATH Keywords

Linear programmingSensitivity analysisRobustInfluence analysisLeast absolute valueMinimum sum of absolute errors regression

Mathematics Subject Classification ID

Point estimation (62F10) Linear inference, regression (62J99) Robustness and adaptive procedures (parametric inference) (62F35) Diagnostics, and linear inference and regression (62J20)

Related Items (3)

Computing multiple-output regression quantile regions ⋮ Sensitivity analysis of constrained linear \(L_1\) regression: perturbations to response and predictor variables ⋮ Computing multiple-output regression quantile regions from projection quantiles

Cites Work

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• The Gaussian hare and the Laplacian tortoise: computability of squared-error versus absolute-error estimators. With comments by Ronald A. Thisted and M. R. Osborne and a rejoinder by the authors
• Interior Analysis for the Minimum Sum of Absolute Errors Regression
• Direct calculation of sensitivity to the coefficients of the basic submatrix in parametric linear programming
• The Minimum Sum of Absolute Errors Regression: A State of the Art Survey
• The Hat Matrix in Regression and ANOVA
• Intervals which Leave the Minimum Sum of Absolute Errors Regression Unchanged

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