Penalization, bias reduction, and default priors in logistic and related categorical and survival regressions

Separation-Resistant and Bias-Reduced Logistic Regression:STATISTICAMacro

(2015) Kamil Fijorek et al.   Journal of Statistical Software

Maximum Likelihood, Profile Likelihood, and Penalized Likelihood: A Primer

(2013) S. R. Cole et al.   AMERICAN JOURNAL OF EPIDEMIOLOGY

Sensitivity Analyses for Sparse-Data Problems—Using Weakly Informative Bayesian Priors

(2013) Ghassan B. Hamra et al.   EPIDEMIOLOGY

Bayesian regression in SAS software

(2012) Sheena G Sullivan et al.   INTERNATIONAL JOURNAL OF EPIDEMIOLOGY

Simpson’s Paradox From Adding Constants in Contingency Tables as an Example of Bayesian Noncollapsibility

(2010) Sander Greenland   AMERICAN STATISTICIAN

Bias-reduced and separation-proof conditional logistic regression with small or sparse data sets

(2010) Georg Heinze et al.   STATISTICS IN MEDICINE

A weakly informative default prior distribution for logistic and other regression models

(2009) Andrew Gelman et al.   Annals of Applied Statistics

The formal definition of reference priors

(2009) James O. Berger et al.   ANNALS OF STATISTICS

Bayesian perspectives for epidemiologic research: III. Bias analysis via missing-data methods

(2009) Sander Greenland   INTERNATIONAL JOURNAL OF EPIDEMIOLOGY

Properties and Implementation of Jeffreys’s Prior in Binomial Regression Models

(2009) Ming-Hui Chen et al.   JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION