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A multiple testing protocol for exploratory data analysis and the local misclassification rate

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  • David D. Watts
  • Joshua D. Habiger

Abstract

A false discovery rate (FDR) procedure is often employed in exploratory data analysis to determine which among thousands or millions of attributes are worthy of follow-up analysis. However, these methods tend to discover the most statistically significant attributes, which need not be the most worthy of further exploration. This article provides a new FDR-controlling method that allows for the nature of the exploratory analysis to be considered when determining which attributes are discovered. To illustrate, a study in which the objective is to classify discoveries into one of several clusters is considered, and a new FDR method that minimizes the misclassification rate is developed. It is shown analytically and with simulation that the proposed method performs better than competing methods.

Suggested Citation

  • David D. Watts & Joshua D. Habiger, 2018. "A multiple testing protocol for exploratory data analysis and the local misclassification rate," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(15), pages 3588-3604, August.
    • Handle: RePEc:taf:lstaxx:v:47:y:2018:i:15:p:3588-3604
    DOI: 10.1080/03610926.2017.1361982
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