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Multivariate and functional robust fusion methods for structured Big Data

Author

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  • Aaron, Catherine
  • Cholaquidis, Alejandro
  • Fraiman, Ricardo
  • Ghattas, Badih

Abstract

We address one of the important problems in Big Data, namely how to combine estimators from different subsamples by robust fusion procedures, when we are unable to deal with the whole sample. We propose a general framework based on the classic idea of ‘divide and conquer’. In particular we address in some detail the case of a multivariate location and scatter matrix, the covariance operator for functional data, and clustering problems.

Suggested Citation

  • Aaron, Catherine & Cholaquidis, Alejandro & Fraiman, Ricardo & Ghattas, Badih, 2019. "Multivariate and functional robust fusion methods for structured Big Data," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 149-161.
    • Handle: RePEc:eee:jmvana:v:170:y:2019:i:c:p:149-161
    DOI: 10.1016/j.jmva.2018.06.012
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    References listed on IDEAS

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    1. David Kraus & Victor M. Panaretos, 2012. "Dispersion operators and resistant second-order functional data analysis," Biometrika, Biometrika Trust, vol. 99(4), pages 813-832.
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    Cited by:

    1. Aneiros, Germán & Cao, Ricardo & Fraiman, Ricardo & Genest, Christian & Vieu, Philippe, 2019. "Recent advances in functional data analysis and high-dimensional statistics," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 3-9.

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