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dc.contributor.authorKing, Gary
dc.contributor.authorZeng, Langche
dc.date.accessioned2010-05-21T20:40:13Z
dc.date.issued2001
dc.identifier.citationKing, Gary, and Langche Zeng. 2001. Logistic regression in rare events data. Political Analysis 9(2): 137-163.en_US
dc.identifier.issn1047-1987en_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:4125045
dc.description.abstractWe study rare events data, binary dependent variables with dozens to thousands of times fewer ones (events, such as wars, vetoes, cases of political activism, or epidemiological infections) than zeros (“nonevents”). In many literatures, these variables have proven difficult to explain and predict, a problem that seems to have at least two sources. First, popular statistical procedures, such as logistic regression, can sharply underestimate the probability of rare events. We recommend corrections that outperform existing methods and change the estimates of absolute and relative risks by as much as some estimated effects reported in the literature. Second, commonly used data collection strategies are grossly inefficient for rare events data. The fear of collecting data with too few events has led to data collections with huge numbers of observations but relatively few, and poorly measured, explanatory variables, such as in international conflict data with more than a quarter-million dyads, only a few of which are at war. As it turns out, more efficient sampling designs exist for making valid inferences, such as sampling all available events (e.g., wars) and a tiny fraction of nonevents (peace). This enables scholars to save as much as 99% of their (nonfixed) data collection costs or to collect much more meaningful explanatory variables.We provide methods that link these two results, enabling both types of corrections to work simultaneously, and software that implements the methods developed.en_US
dc.description.sponsorshipGovernmenten_US
dc.language.isoen_USen_US
dc.publisherOxford University Pressen_US
dc.relation.isversionofhttp://www.jstatsoft.org/v08/i02en_US
dc.relation.hasversionhttp://gking.harvard.edu/files/0s.pdfen_US
dash.licenseLAA
dc.titleLogistic Regression in Rare Events Dataen_US
dc.typeJournal Articleen_US
dc.description.versionProofen_US
dc.relation.journalPolitical Analysis -Ann Arbor then Oxford-en_US
dash.depositing.authorKing, Gary
dc.date.available2010-05-21T20:40:13Z
dash.identifier.orcid0000-0002-5327-7631*
dash.contributor.affiliatedKing, Gary
dc.identifier.orcid0000-0002-5327-7631


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