Sensitivity and specificity of information criteria

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dc.contributor.authorDziak, John J
dc.contributor.authorCoffman, Donna L
dc.contributor.authorLanza, Stephanie T
dc.contributor.authorLi, Runze
dc.contributor.authorJermiin, Lars
dc.date.accessioned2021-02-23T02:59:13Z
dc.date.issued2020
dc.date.updated2020-11-15T07:18:54Z
dc.description.abstractInformation criteria (ICs) based on penalized likelihood, such as Akaike’s information criterion (AIC), the Bayesian information criterion (BIC) and sample-size-adjusted versions of them, are widely used for model selection in health and biological research. However, different criteria sometimes support different models, leading to discussions about which is the most trustworthy. Some researchers and fields of study habitually use one or the other, often without a clearly stated justification. They may not realize that the criteria may disagree. Others try to compare models using multiple criteria but encounter ambiguity when different criteria lead to substantively different answers, leading to questions about which criterion is best. In this paper we present an alternative perspective on these criteria that can help in interpreting their practical implications. Specifically, in some cases the comparison of two models using ICs can be viewed as equivalent to a likelihood ratio test, with the different criteria representing different alpha levels and BIC being a more conservative test than AIC. This perspective may lead to insights about how to interpret the ICs in more complex situations. For example, AIC or BIC could be preferable, depending on the relative importance one assigns to sensitivity versus specificity. Understanding the differences and similarities among the ICs can make it easier to compare their results and to use them to make informed decisions.en_AU
dc.description.sponsorshipNational Institute on Drug Abuse (NIH grant P50 DA039838). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute on Drug Abuse or the National Institutes of Health.en_AU
dc.format.mimetypeapplication/pdfen_AU
dc.identifier.issn1467-5463en_AU
dc.identifier.urihttp://hdl.handle.net/1885/224152
dc.language.isoen_AUen_AU
dc.publisherBritish Academy and Oxford University Pressen_AU
dc.rights© The Author(s) 2019en_AU
dc.sourceBriefings in Bioinformaticsen_AU
dc.source.urihttps://academic.oup.com/bib/article/21/2/553/5380417en_AU
dc.subjectAkaike information criterionen_AU
dc.subjectBayesian information criterionen_AU
dc.subjectlatent class analysis;en_AU
dc.subjectlikelihood ratio testing;en_AU
dc.titleSensitivity and specificity of information criteriaen_AU
dc.typeJournal articleen_AU
local.bibliographicCitation.issue2en_AU
local.bibliographicCitation.lastpage565en_AU
local.bibliographicCitation.startpage553en_AU
local.contributor.affiliationDziak, John J, Pennsylvania State Universityen_AU
local.contributor.affiliationCoffman, Donna L, Temple Universityen_AU
local.contributor.affiliationLanza, Stephanie T, Edna Bennett Pierce Prevention Research Centeren_AU
local.contributor.affiliationLi, Runze, Pennsylvania State Universityen_AU
local.contributor.affiliationJermiin, Lars, College of Science, ANUen_AU
local.contributor.authoremailu5268558@anu.edu.auen_AU
local.contributor.authoruidJermiin, Lars, u5268558en_AU
local.description.embargo2099-12-31
local.description.notesImported from ARIESen_AU
local.identifier.absfor010401 - Applied Statisticsen_AU
local.identifier.absseo970106 - Expanding Knowledge in the Biological Sciencesen_AU
local.identifier.ariespublicationa383154xPUB13678en_AU
local.identifier.citationvolume21en_AU
local.identifier.doi10.1093/bib/bbz016en_AU
local.identifier.uidSubmittedBya383154en_AU
local.publisher.urlhttps://academic.oup.com/bib/article/21/2/553/5380417en_AU
local.type.statusPublished Versionen_AU

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