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Robust tests for linear regression models based on τ-estimates

Robust tests for linear regression models based on \(\tau\)-estimates
descriptionPublicationkeyboard_double_arrow_right Article 01 Jan 2016 English Publisher:Elsevier BVJournal:Computational Statistics & Data Analysis, volume 93, pages 436-455 (issn: 0167-9473, Copyright policy )Funded by:NSERC | unidentified
Authors: Salibian-Barrera, Matias; Van Aelst, Stefan; Yohai, Victor J;

doi: 10.1016/j.csda.2014.09.012

handle: 11336/68009 , 1854/LU-8513431

Robust tests for linear regression models based on τ-estimates

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Keywords

Linear regression; mixed models, Analysis of variance and covariance (ANOVA), Robust statistics, Linear Regression, Robust Tests, Mathematics and Statistics, S-ESTIMATORS, BOOTSTRAP, robust statistics, Robust tests, HIGH BREAKDOWN-POINT, linear regression, BOUNDED-INFLUENCE TESTS, https://purl.org/becyt/ford/1.1, robust tests, Robust Statistics, Robustness and adaptive procedures (parametric inference), Linear regression, https://purl.org/becyt/ford/1, Computational methods for problems pertaining to statistics, SCALE

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
11
Average
Average
Top 10%
Green
bronze