Electronic Journal of Statistics
- Electron. J. Statist.
- Volume 11, Number 1 (2017), 215-240.
Large-scale mode identification and data-driven sciences
Bump-hunting or mode identification is a fundamental problem that arises in almost every scientific field of data-driven discovery. Surprisingly, very few data modeling tools are available for automatic (not requiring manual case-by-case investigation), objective (not subjective), and nonparametric (not based on restrictive parametric model assumptions) mode discovery, which can scale to large data sets. This article introduces LPMode–an algorithm based on a new theory for detecting multimodality of a probability density. We apply LPMode to answer important research questions arising in various fields from environmental science, ecology, econometrics, analytical chemistry to astronomy and cancer genomics.
Electron. J. Statist., Volume 11, Number 1 (2017), 215-240.
Received: August 2016
First available in Project Euclid: 3 February 2017
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Mukhopadhyay, Subhadeep. Large-scale mode identification and data-driven sciences. Electron. J. Statist. 11 (2017), no. 1, 215--240. doi:10.1214/17-EJS1229. https://projecteuclid.org/euclid.ejs/1486090845
- Supplementary appendix to “Large-scale mode identification and data-driven sciences”. Figures 6–14 referenced in Section 4 are available as Supplementary appendix.