Mode hunting through active information

Daniel Andrés Díaz‐Pachón, Juan Pablo Sáenz, J. Sunil Rao and Jean‐Eudes Dazard

Applied Stochastic Models in Business and Industry, 2019, vol. 35, issue 2, 376-393

Abstract: We propose a new method to find modes based on active information. We develop an algorithm called active information mode hunting (AIMH) that, when applied to the whole space, will say whether there are any modes present and where they are. We show AIMH is consistent and, given that information increases where probability decreases, it helps to overcome issues with the curse of dimensionality. The AIMH also reduces the dimensionality with no resource to principal components. We illustrate the method in three ways: with a theoretical example (showing how it performs better than other mode hunting strategies), a real dataset business application, and a simulation.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:wly:apsmbi:v:35:y:2019:i:2:p:376-393

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