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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.<\/jats:p>","DOI":"10.1002\/asmb.2430","type":"journal-article","created":{"date-parts":[[2019,2,1]],"date-time":"2019-02-01T02:44:33Z","timestamp":1548989073000},"page":"376-393","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Mode hunting through active information"],"prefix":"10.1002","volume":"35","author":[{"given":"Daniel Andr\u00e9s","family":"D\u00edaz\u2010Pach\u00f3n","sequence":"first","affiliation":[{"name":"Division of Biostatistics, Don Soffer Clinical Research Center University of Miami  Miami Florida"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan Pablo","family":"S\u00e1enz","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering University of Miami  Coral Gables Florida"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J. 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