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Big Data in context and robustness against heterogeneity

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  • Marron, J.S.

Abstract

The phrase Big Data has generated substantial current discussion within and outside of the field of statistics. Some personal observations about this phenomenon are discussed. One contribution is to put this set of ideas into a larger historical context. Another is to point out the related important concept of robustness against data heterogeneity, and some earlier methods which had that property, and also to discuss a number of interesting open problems motivated by this concept.

Suggested Citation

  • Marron, J.S., 2017. "Big Data in context and robustness against heterogeneity," Econometrics and Statistics, Elsevier, vol. 2(C), pages 73-80.
    • Handle: RePEc:eee:ecosta:v:2:y:2017:i:c:p:73-80
    DOI: 10.1016/j.ecosta.2016.06.001
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    References listed on IDEAS

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    1. Peter Hall & J. S. Marron & Amnon Neeman, 2005. "Geometric representation of high dimension, low sample size data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(3), pages 427-444, June.
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    4. Jeffrey T Leek & John D Storey, 2007. "Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis," PLOS Genetics, Public Library of Science, vol. 3(9), pages 1-12, September.
    5. Charles M. Perou & Therese Sørlie & Michael B. Eisen & Matt van de Rijn & Stefanie S. Jeffrey & Christian A. Rees & Jonathan R. Pollack & Douglas T. Ross & Hilde Johnsen & Lars A. Akslen & Øystein Flu, 2000. "Molecular portraits of human breast tumours," Nature, Nature, vol. 406(6797), pages 747-752, August.
    6. Makoto Aoshima & Kazuyoshi Yata, 2014. "A distance-based, misclassification rate adjusted classifier for multiclass, high-dimensional data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(5), pages 983-1010, October.
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    Keywords

    Big data; Robustness against heterogeneity;

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