Approximate Bayesian logistic regression via penalized likelihood estimation with data augmentation

Andrea Discacciati

Italian Stata Users' Group Meetings 2014 from Stata Users Group

Abstract: Data augmentation is a technique for conducting approximate Bayesian regression analysis. This technique is a form of penalized likelihood estimation where prior information, represented by one or more specific prior data records, generates a penalty function that imposes the desired priors on the regression coefficients. We present a new command, penlogit, that fits penalized logistic regression via data augmentation. We illustrate the command through an example using data from an epidemiological study.

Date: 2014-11-13
New Economics Papers: this item is included in nep-ecm
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Persistent link: https://EconPapers.repec.org/RePEc:boc:isug14:03

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