Performance of Firth-and logF-type penalized methods in risk prediction for small or sparse binary d...
Performance of Firth-and logF-type penalized methods in risk prediction for small or sparse binary data
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Publisher
England: BioMed Central Ltd
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English
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England: BioMed Central Ltd
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When developing risk models for binary data with small or sparse data sets, the standard maximum likelihood estimation (MLE) based logistic regression faces several problems including biased or infinite estimate of the regression coefficient and frequent convergence failure of the likelihood due to separation. The problem of separation occurs commo...
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Performance of Firth-and logF-type penalized methods in risk prediction for small or sparse binary data
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TN_cdi_doaj_primary_oai_doaj_org_article_a6467d4b281348f2a9278fd03a5a4500
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_a6467d4b281348f2a9278fd03a5a4500
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ISSN
1471-2288
E-ISSN
1471-2288
DOI
10.1186/s12874-017-0313-9
