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

David D. Watts and Joshua D. Habiger

Communications in Statistics - Theory and Methods, 2018, vol. 47, issue 15, 3588-3604

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.

Date: 2018
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DOI: 10.1080/03610926.2017.1361982

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