An Object-Oriented Framework for Robust Multivariate Analysis
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Taking advantage of the S4 class system of the programming environment R, which facilitates the creation and maintenance of reusable and modular components, an object-oriented framework for robust multivariate analysis was developed. The framework resides in the packages robustbase and rrcov and includes an almost complete set of algorithms for computing robust multivariate location and scatter, various robust methods for principal component analysis as well as robust linear and quadratic discriminant analysis. The design of these methods follows common patterns which we call statistical design patterns in analogy to the design patterns widely used in software engineering. The application of the framework to data analysis as well as possible extensions by the development of new methods is demonstrated on examples which themselves are part of the package rrcov.
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All regressions involving the discount rate were robust to account for possible outliers.
“To take into account the effect of possible outliers, all regressions with the discount rate were robust [using the robustbase package in R (76)].”
From Adolescent impatience decreases with increased frontostriatal connectivity (2015) · cites this paper as [76]
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- Authors
- Todorov, V., & Filzmoser, P.
- ORCID
- Todorov, Valentin
- Venue
- Journal of Statistical Software
- Publisher
- Foundation for Open Access Statistic
- Published
- 2009
- Volume
- 32
- Issue
- 3
- ISSN
- 1548-7660 (ISSN-L)
- OpenAlex
- W2157324002
- Cited by
- 420
- References
- 54
- Citations by year
- 2026: 27 · 2025: 43 · 2024: 39 · 2023: 28 · 2022: 18 · 2021: 25 · 2020: 39 · 2019: 29 · 2018: 27 · 2017: 20 · 2016: 32 · 2015: 23 · 2014: 22 · 2013: 17 · 2012: 16
- FWCI
- 9.43
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Journal of Statistical Software (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Physical Sciences › Computer Science › Artificial Intelligence › Data Analysis with R
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Keywords
- object-oriented design, R programming language, S4 classes, robust principal component analysis, robust discriminant analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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