Rejecting outliers and estimating errors in an orthogonal-regression framework

DOI10.1098/rsta.1995.0022zbMath0846.62054OpenAlexW1983520352MaRDI QIDQ4833616

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Publication date: 3 October 1996

Published in: Philosophical Transactions of the Royal Society of London. Series A: Physical and Engineering Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1098/rsta.1995.0022

zbMATH Keywords

outliersGaussian noiseleast squares minimizationrobust statisticsresidualsorthogonal regressionmatrix perturbation theoryeigendecompositionsaffine epipolar geometrycomputer vision applicationhyperplane covariance matrix

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Robustness and adaptive procedures (parametric inference) (62F35) Applications of statistics (62P99) Diagnostics, and linear inference and regression (62J20)

Related Items (2)

Maximum likelihood robust regression by mixture models ⋮ A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry

Uses Software

• alr3

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