Matlab library LIBRA. LIBRA: a MATLAB Library for Robust Analysis is developed at ROBUST@Leuven, the research group on robust statistics at the KU Leuven. It contains user-friendly implementations of several robust procedures. These methods are resistant to outliers in the data. Currently, the library contains functions for univariate location, scale and skewness, multivariate location and covariance estimation (MCD), regression (LTS, MCD-regression), Principal Component Analysis (RAPCA, ROBPCA), Principal Component Regression (RPCR), Partial Least Squares Regression (RSIMPLS), classification (RDA, RSIMCA), clustering, outlier detection for skewed data (including the bagplot based on halfspace depth), and censored depth quantiles. For comparison also several non-robust functions are included. Many graphical tools are provided for model checking and outlier detection. Most of the functions require the MATLAB Statistics Toolbox.

References in zbMATH (referenced in 21 articles )

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  1. Hubert, Mia; Gijbels, Irène; Vanpaemel, Dina: Reducing the mean squared error of quantile-based estimators by smoothing (2013)
  2. Turkmen, Asuman; Billor, Nedret: Partial least squares classification for high dimensional data using the PCOUT algorithm (2013)
  3. Slaets, Leen; Claeskens, Gerda; Hubert, Mia: Phase and amplitude-based clustering for functional data (2012)
  4. Torti, Francesca; Perrotta, Domenico; Atkinson, Anthony C.; Riani, Marco: Benchmark testing of algorithms for very robust regression: FS, LMS and LTS (2012)
  5. Bavaud, François: On the Schoenberg transformations in data analysis: theory and illustrations (2011)
  6. Debruyne, Michiel; Verdonck, Tim: Robust kernel principal component analysis and classification (2010)
  7. Hsu, Chun-Chin; Chen, Long-Sheng; Liu, Cheng-Hsiang: A process monitoring scheme based on independent component analysis and adjusted outliers (2010)
  8. Hubert, Mia; Van der Veeken, Stephan: Robust classification for skewed data (2010)
  9. Nguyen, T.D.; Welsch, R.: Outlier detection and least trimmed squares approximation using semi-definite programming (2010)
  10. Nguyen, Tri-Dzung; Welsch, Roy E.: Outlier detection and robust covariance estimation using mathematical programming (2010)
  11. Hubert, Mia; Rousseeuw, Peter; Verdonck, Tim: Robust PCA for skewed data and its outlier map (2009)
  12. Noponen, Kai; Kortelainen, Jukka; Seppänen, Tapio: Invariant trajectory classification of dynamical systems with a case study on ECG (2009)
  13. Serneels, Sven; Verdonck, Tim: Principal component regression for data containing outliers and missing elements (2009)
  14. Chen, Dechang; Lu, Chang-Tien; Kou, Yufeng; Chen, Feng: On detecting spatial outliers (2008) ioport
  15. Debruyne, M.; Hubert, M.; Portnoy, S.; Branden, K.Vanden: Censored depth quantiles (2008)
  16. Hubert, M.; Vandervieren, E.: An adjusted boxplot for skewed distributions (2008)
  17. Serneels, Sven; Verdonck, Tim: Principal component analysis for data containing outliers and missing elements (2008)
  18. Weston, David J.; Hand, David J.; Adams, Niall M.; Whitrow, Christopher; Juszczak, Piotr: Plastic card fraud detection using peer group analysis (2008)
  19. Xue, Jing-Hao; Titterington, D.Michael: Comment on “On discriminative vs. Generative classifiers: A comparison of logistic regression and naive bayes” (2008) ioport
  20. Hubert, Mia; Engelen, Sanne: Fast cross-validation of high-breakdown resampling methods for PCA (2007)

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