FSDA: a MATLAB toolbox for robust analysis and interactive data exploration. We present the FSDA (Forward Search for Data Analysis) toolbox, a new software library that extends MATLAB and its Statistics Toolbox to support a robust and efficient analysis of complex datasets, affected by different sources of heterogeneity. As the name of the library indicates, the project was born around the Forward Search approach, but it has evolved to include the main traditional robust multivariate and regression techniques, including LMS, LTS, MCD, MVE, MM and S estimation. To address problems where data deviate from typical model assumptions, tools are available for robust data transformation and robust model selection. When different views of the data are available, e.g. a scatterplot of units and a plot of distances of such units from a fitted model, FSDA links such views and offers the possibility to interact with them. For example, selections of objects in a plot are highlighted in the other plots. This considerably simplifies the exploration of the data in view of extracting information and detecting patterns. We show the potential of the FSDA in chemometrics using data from chemical and pharmaceutical problems, where the presence of outliers, multiple groups, deviations from normality and other complex structures is not an exceptional circumstance.

References in zbMATH (referenced in 17 articles )

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  1. Bagdonavičius, Vilijandas; Petkevičius, Linas: A new multiple outliers identification method in linear regression (2020)
  2. García-Escudero, Luis Angel; Mayo-Iscar, Agustín; Riani, Marco: Model-based clustering with determinant-and-shape constraint (2020)
  3. Torti, Francesca; Perrotta, Domenico; Riani, Marco; Cerioli, Andrea: Assessing trimming methodologies for clustering linear regression data (2019)
  4. Perrotta, Domenico; Torti, Francesca: Discussion of “The power of monitoring: how to make the most of a contaminated multivariate sample” (2018)
  5. Raymaekers, Jakob; Rousseeuw, Peter J.; Vranckx, Iwein: Discussion of: “The power of monitoring: how to make the most of a contaminated multivariate sample” (2018)
  6. Todorov, Valentin: Discussion of “The power of monitoring: how to make the most of a contaminated multivariate sample” (2018)
  7. Bulut, Hasan; Öner, Yüksel: The evaluation of socio-economic development of development agency regions in Turkey using classical and robust principal component analyses (2017)
  8. De Bin, Riccardo; Boulesteix, Anne-Laure; Sauerbrei, Willi: Detection of influential points as a byproduct of resampling-based variable selection procedures (2017)
  9. Atkinson, Anthony C.; Riani, Marco; Torti, Francesca: Robust methods for heteroskedastic regression (2016)
  10. Cerioli, Andrea; Atkinson, Anthony C.; Riani, Marco: How to marry robustness and applied statistics (2016)
  11. Hubert, Mia; Rousseeuw, Peter; Vanpaemel, Dina; Verdonck, Tim: The DetS and DetMM estimators for multivariate location and scatter (2015)
  12. Riani, Marco; Cerioli, Andrea; Perrotta, Domenico; Torti, Francesca: Simulating mixtures of multivariate data with fixed cluster overlap in FSDA library (2015)
  13. Riani, Marco; Atkinson, Anthony C.; Perrotta, Domenico: A parametric framework for the comparison of methods of very robust regression (2014)
  14. Riani, Marco; Cerioli, Andrea; Atkinson, Anthony C.; Perrotta, Domenico: Monitoring robust regression (2014)
  15. Riani, Marco; Cerioli, Andrea; Torti, Francesca: On consistency factors and efficiency of robust (S)-estimators (2014)
  16. Huang, Yufen; Wang, Sheng-Wen: Influence analysis on the direction of optimal response (2013)
  17. Turchi, Marco; Perrotta, Domenico; Riani, Marco; Cerioli, Andrea: Robustness issues in text mining (2013) ioport