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 26 articles )

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  1. Luca, Stijn E.; Pimentel, Marco A. F.; Watkinson, Peter J.; Clifton, David A.: Point process models for novelty detection on spatial point patterns and their extremes (2018)
  2. Maronna, Ricardo A.; Yohai, Victor J.: Robust and efficient estimation of multivariate scatter and location (2017)
  3. Huang, Xiaolin; Shi, Lei; Pelckmans, Kristiaan; Suykens, Johan A. K.: Asymmetric (\nu)-tube support vector regression (2014)
  4. Hubert, Mia; Gijbels, Irène; Vanpaemel, Dina: Reducing the mean squared error of quantile-based estimators by smoothing (2013)
  5. Turkmen, Asuman; Billor, Nedret: Partial least squares classification for high dimensional data using the PCOUT algorithm (2013)
  6. Slaets, Leen; Claeskens, Gerda; Hubert, Mia: Phase and amplitude-based clustering for functional data (2012)
  7. Torti, Francesca; Perrotta, Domenico; Atkinson, Anthony C.; Riani, Marco: Benchmark testing of algorithms for very robust regression: FS, LMS and LTS (2012)
  8. Bavaud, François: On the Schoenberg transformations in data analysis: theory and illustrations (2011)
  9. Debruyne, Michiel; Verdonck, Tim: Robust kernel principal component analysis and classification (2010)
  10. Hsu, Chun-Chin; Chen, Long-Sheng; Liu, Cheng-Hsiang: A process monitoring scheme based on independent component analysis and adjusted outliers (2010)
  11. Hubert, Mia; Van der Veeken, Stephan: Robust classification for skewed data (2010)
  12. Nguyen, T. D.; Welsch, R.: Outlier detection and least trimmed squares approximation using semi-definite programming (2010)
  13. Nguyen, Tri-Dzung; Welsch, Roy E.: Outlier detection and robust covariance estimation using mathematical programming (2010)
  14. Verdonck, Tim; Hubert, Mia; Rousseeuw, Peter J.: DetMCD in a calibration framework (2010)
  15. Hubert, Mia; Rousseeuw, Peter; Verdonck, Tim: Robust PCA for skewed data and its outlier map (2009)
  16. Noponen, Kai; Kortelainen, Jukka; Seppänen, Tapio: Invariant trajectory classification of dynamical systems with a case study on ECG (2009)
  17. Serneels, Sven; Verdonck, Tim: Principal component regression for data containing outliers and missing elements (2009)
  18. Chen, Dechang; Lu, Chang-Tien; Kou, Yufeng; Chen, Feng: On detecting spatial outliers (2008) ioport
  19. Debruyne, M.; Hubert, M.; Portnoy, S.; Vanden Branden, K.: Censored depth quantiles (2008)
  20. Hubert, Mia; Rousseeuw, Peter J.; Van Aelst, Stefan: High-breakdown robust multivariate methods (2008)

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