Approximate Bayesian logistic regression via penalized likelihood by data augmentation
Andrea Discacciati
Unit of Biostatistics and Unit of Nutritional Epidemiology
Institute of Environmental Medicine
Karolinska Institutet
Stockholm, Sweden
[email protected]
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Nicola Orsini
Unit of Biostatistics and Unit of Nutritional Epidemiology
Institute of Environmental Medicine
Karolinska Institutet
Stockholm, Sweden
[email protected]
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Sander Greenland
Departments of Epidemiology and Statistics
University of California
Los Angeles, CA
[email protected]
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Abstract. We present a command, penlogit, for approximate Bayesian logistic
regression using penalized likelihood estimation via data augmentation.
This command automatically adds specific prior-data records to a
dataset. These records are computed so that they generate a penalty
function for the log likelihood of a logistic model, which equals (up to
an additive constant) a set of independent log prior distributions on
the model parameters. This command overcomes the necessity of relying on
specialized software and statistical tools (such as Markov chain Monte
Carlo) for fitting Bayesian models, and allows one to assess the
information content of a prior in terms of the data that would be
required to generate the prior as a likelihood function. The command
produces data equivalent to normal and generalized log-F priors for the
model parameters, providing flexible translation of background
information into prior data, which allows calculation of approximate
posterior medians and intervals from ordinary maximum likelihood
programs. We illustrate the command through an example using data from
an observational study of neonatal mortality.
View all articles by these authors:
Andrea Discacciati, Nicola Orsini, Sander Greenland
View all articles with these keywords:
penlogit, penalized likelihood estimation, data augmentation, Bayesian methods, logistic models
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