Resistant lower rank approximation of matrices by iterative majorization

DOI10.1016/0167-9473(94)90163-5zbMath0825.65128OpenAlexW2126555845WikidataQ126339572 ScholiaQ126339572MaRDI QIDQ1896200

Willem J. Heiser, Peter Verboon

Publication date: 17 August 1995

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

Full work available at URL: https://doi.org/10.1016/0167-9473(94)90163-5

zbMATH Keywords

robustnessresistancemajorizationiteratively reweighted least squaresHuber functionlower rank approximationbiweight function

Mathematics Subject Classification ID

Software, source code, etc. for problems pertaining to statistics (62-04) Probabilistic methods, stochastic differential equations (65C99)

Related Items

Robust low-rank data matrix approximations ⋮ Weighted least squares fitting using ordinary least squares algorithms ⋮ A unified approach to exploratory factor analysis with missing data, nonnormal data, and in the presence of outliers ⋮ Robust mean and covariance structure analysis through iteratively reweighted least squares ⋮ Sharp quadratic majorization in one dimension ⋮ Applied regression analysis bibliography update 1994-97 ⋮ Pairwise-comparison methods in multiple objective programming, with applications in a long-term energy-planning model

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• The Fitting of Power Series, Meaning Polynomials, Illustrated on Band-Spectroscopic Data
• Robust Estimation of a Location Parameter
• Some Applications of the Singular Decomposition of a Matrix
• The biplot graphic display of matrices with application to principal component analysis
• Robust Statistics

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