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Classification Error of the Thresholded Independence Rule

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  • Britta Anker Bak
  • Jens Ledet Jensen
  • Morten Fenger-Grøn

Abstract

type="main" xml:id="sjos12093-abs-0001"> We consider classification in the situation of two groups with normally distributed data in the ‘large p small n’ framework. To counterbalance the high number of variables, we consider the thresholded independence rule. An upper bound on the classification error is established that is taylored to a mean value of interest in biological applications.

Suggested Citation

  • Britta Anker Bak & Jens Ledet Jensen & Morten Fenger-Grøn, 2015. "Classification Error of the Thresholded Independence Rule," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(1), pages 32-42, March.
    • Handle: RePEc:bla:scjsta:v:42:y:2015:i:1:p:32-42
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    File URL: http://hdl.handle.net/10.1111/sjos.12093
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    Cited by:

    1. Bak, Britta Anker & Jensen, Jens Ledet, 2016. "High dimensional classifiers in the imbalanced case," Computational Statistics & Data Analysis, Elsevier, vol. 98(C), pages 46-59.

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