Open Access
2017 Large-scale mode identification and data-driven sciences
Subhadeep Mukhopadhyay
Electron. J. Statist. 11(1): 215-240 (2017). DOI: 10.1214/17-EJS1229


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.


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Subhadeep Mukhopadhyay. "Large-scale mode identification and data-driven sciences." Electron. J. Statist. 11 (1) 215 - 240, 2017.


Received: 1 August 2016; Published: 2017
First available in Project Euclid: 3 February 2017

zbMATH: 1356.62052
MathSciNet: MR3605036
Digital Object Identifier: 10.1214/17-EJS1229

Primary: 62G07 , 62G30 , 62G86

Keywords: bump(s) above background , connector density , large-scale mode exploration , multidisciplinary sciences , nonparametric exploratory modeling , orthogonal rank polynomials , Skew-G modeling

Vol.11 • No. 1 • 2017
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