Approximate Bayesian Logistic Regression via Penalized Likelihood by Data Augmentation
نویسندگان
چکیده
منابع مشابه
Approximate Bayesian logistic regression via penalized likelihood by data augmentation
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 ...
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ژورنال
عنوان ژورنال: The Stata Journal: Promoting communications on statistics and Stata
سال: 2015
ISSN: 1536-867X,1536-8734
DOI: 10.1177/1536867x1501500306