Precluding rare outcomes by predicting their absence


Autoři: Eric W. Schoon aff001;  David Melamed aff001;  Ronald L. Breiger aff002;  Eunsung Yoon aff002;  Christopher Kleps aff001
Působiště autorů: Department of Sociology, The Ohio State University, Columbus, Ohio, United States of America aff001;  School of Sociology, University of Arizona, Tucson, Arizona, United States of America aff002
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0223239

Souhrn

Forecasting 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 bootstrap inferential test illustrates how our approach can also yield novel substantive insights that are obscured by standard statistical analyses.

Klíčová slova:

Algorithms – Experimental design – Forecasting – Monte Carlo method – Sociolinguistics – Statistical data – Statistical distributions – Analysts


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Článek vyšel v časopise

PLOS One


2019 Číslo 10

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