An Object-Oriented Framework for Robust Multivariate Analysis

By Valentin Todorov, Peter Filzmoser. 2009

435 citations recorded by the source corpus.

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.

Subfields: Analytical Chemistry, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/54b4241b021594cdb20091c95f121f75653ffc36

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In Analytical Chemistry

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Citations in the corpus

Robust statistical methods in R using the WRS2 package (2019, methodology): “Regarding multivariate methods, the rrcov package (Todorov and Filzmoser 2009) provides various implementations such as robust multivariate variancecovariance estimation and robust principal components analysis (PCA).”
A Toolbox for Nonlinear Regression in R: The Package nlstools (2015, methodology): “, using the function nlrob() in package robustbase (Rousseeuw et al. 2015; Todorov and Filzmoser 2009).”
Least quantile regression via modern optimization (2013, methodology): “We considered a few real-world datasets popularly used in the context of robust statistical estimation, as available from the R package robustbase (Rousseeuw et al., 2013; Todorov and Filzmoser, 2009).”
Do we need hundreds of classifiers to solve real world classification problems? (2014, methodology): “…random forest implemented in R using the randomForest and caret packages), with rank 32.9, average accuracy 82.0%(±16.3) and κ=63.5%(±30.6), followed by rf t (random forest using the randomForest package and tuned with caret), with rank 33.1 and the highest accuracy 82.3%(±15.3) and κ=63.6(±30.0).”
mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models (2016, methodology): “All the datasets used in the examples are available in mclust or in other R packages, such as gclus (Hurley, 2012), rrcov (Todorov and Filzmoser, 2009) and tourr (Wickham et al.”
What matters in differences between life trajectories: a comparative review of sequence dissimilarity measures (2016, background): “) The duration and timing axes roughly correspond to the first two robust principal components (Todorov and Filzmoser, 2009) that were found in Studer (2012).”