wbacon: weighted BACON algorithms for multivariate outlier nomination (detection) and robust linear regression
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2021
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01A - Journal article
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The Journal of Open Source Software
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6
Issue / Number
62
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Open Journals
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Abstract
Outlier nomination (detection) and robust regression are computationally hard problems. This is all the more true when the number of variables and observations grow rapidly. Among all candidate methods, the two BACON (blocked adaptive computationally efficient outlier nominators) algorithms of Billor et al.(2000) have favorable computational characteristics as they require only a few model evaluations irrespective of the sample size. This makes them popular algorithms for multivariate outlier nomination/detection and robust linear regression (at the time of writing Google Scholar reports more than 500 citations of the Billor et al.(2000) paper). wbacon is a package for the R statistical software (R Core Team, 2021). It is aimed at medium to large data sets that can possibly have (sampling) weights (e.g., data from complex survey samples). The package has a user-friendly Rinterface (with plotting methods, etc.) and is written mainly in the C language (with OpenMP support for parallelization; see OpenMP Architecture Review Board(2018)) for performance reasons.
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330 - Wirtschaft
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2475-9066
Language
English
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Yes
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Published
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Diamond
Citation
SCHOCH, Tobias, 2021. wbacon: weighted BACON algorithms for multivariate outlier nomination (detection) and robust linear regression. The Journal of Open Source Software. 2021. Bd. 6, Nr. 62. DOI 10.21105/joss.03238. Verfügbar unter: https://doi.org/10.26041/fhnw-7025