AffiliationUniv Arizona, Sch Sociol
MetadataShow full item record
PublisherPUBLIC LIBRARY SCIENCE
CitationSchoon, E. W., Melamed, D., Breiger, R. L., Yoon, E., & Kleps, C. (2019). Precluding rare outcomes by predicting their absence. PloS one, 14(10), e0223239.
Rights© 2019 Schoon et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at firstname.lastname@example.org.
AbstractForecasting extremely rare events is a pressing problem, but efforts to model such outcomes are often limited by the presence of multiple causes within classes of events, insufficient observations of the outcome to assess fit, and biased estimates due to insufficient observations of the outcome. We introduce a novel approach for analyzing rare event data that addresses these challenges by turning attention to the conditions under which rare outcomes do not occur. We detail how configurational methods can be used to identify conditions or sets of conditions that would preclude the occurrence of a rare outcome. Results from Monte Carlo experiments show that our approach can be used to systematically preclude up to 78.6% of observations, and application to ground-truth data coupled with a boot-strap inferential test illustrates how our approach can also yield novel substantive insights that are obscured by standard statistical analyses.
NoteOpen access journal
VersionFinal published version
Except where otherwise noted, this item's license is described as © 2019 Schoon et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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