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Tools for Exploring Multivariate Data: The Package ICS

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  • Nordhausen, Klaus
  • Oja, Hannu
  • Tyler, David E.

Abstract

Invariant coordinate selection (ICS) has recently been introduced as a method for exploring multivariate data. It includes as a special case a method for recovering the unmixing matrix in independent components analysis (ICA). It also serves as a basis for classes of multivariate nonparametric tests, and as a tool in cluster analysis or blind discrimination. The aim of this paper is to briefly explain the (ICS) method and to illustrate how various applications can be implemented using the R package ICS. Several examples are used to show how the ICS method and ICS package can be used in analyzing a multivariate data set.

Suggested Citation

  • Nordhausen, Klaus & Oja, Hannu & Tyler, David E., 2008. "Tools for Exploring Multivariate Data: The Package ICS," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i06).
    • Handle: RePEc:jss:jstsof:v:028:i06
    DOI: http://hdl.handle.net/10.18637/jss.v028.i06
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    Citations

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    Cited by:

    1. Archimbaud, Aurore & Nordhausen, Klaus & Ruiz-Gazen, Anne, 2018. "ICS for multivariate outlier detection with application to quality control," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 184-199.
    2. Chowdhury, Joydeep & Dutta, Subhajit & Arellano-Valle, Reinaldo B. & Genton, Marc G., 2022. "Sub-dimensional Mardia measures of multivariate skewness and kurtosis," Journal of Multivariate Analysis, Elsevier, vol. 192(C).
    3. Alashwali, Fatimah & Kent, John T., 2016. "The use of a common location measure in the invariant coordinate selection and projection pursuit," Journal of Multivariate Analysis, Elsevier, vol. 152(C), pages 145-161.
    4. Klaus Nordhausen & Anne Ruiz-Gazen, 2022. "On the usage of joint diagonalization in multivariate statistics," Post-Print hal-04296111, HAL.
    5. Jorge M. Arevalillo & Hilario Navarro, 2021. "Skewness-Kurtosis Model-Based Projection Pursuit with Application to Summarizing Gene Expression Data," Mathematics, MDPI, vol. 9(9), pages 1-18, April.
    6. Fischer, Daniel & Berro, Alain & Nordhausen, Klaus & Ruiz-Gazen, Anne, 2019. "REPPlab: An R package for detecting clusters and outliers using exploratory projection pursuit," TSE Working Papers 19-1001, Toulouse School of Economics (TSE).
    7. Nordhausen, Klaus & Oja, Hannu & Tyler, David E., 2022. "Asymptotic and bootstrap tests for subspace dimension," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    8. Daniel Fischer & Alain Berro & Klaus Nordhausen & Anne Ruiz-Gazen, 2021. "REPPlab: An R package for detecting clusters and outliers using exploratory projection pursuit," Post-Print hal-03548865, HAL.
    9. Dümbgen, Lutz & Nordhausen, Klaus & Schuhmacher, Heike, 2016. "New algorithms for M-estimation of multivariate scatter and location," Journal of Multivariate Analysis, Elsevier, vol. 144(C), pages 200-217.
    10. Klaus Nordhausen, 2014. "On robustifying some second order blind source separation methods for nonstationary time series," Statistical Papers, Springer, vol. 55(1), pages 141-156, February.
    11. Nordhausen, Klaus & Ruiz-Gazen, Anne, 2022. "On the usage of joint diagonalization in multivariate statistics," Journal of Multivariate Analysis, Elsevier, vol. 188(C).

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