Electronic Journal of Statistics

Large-scale mode identification and data-driven sciences

Subhadeep Mukhopadhyay

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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.

Article information

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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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62G07: Density estimation 62G30: Order statistics; empirical distribution functions 62G86: Nonparametric inference and fuzziness

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

Creative Commons Attribution 4.0 International License.


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

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