Bias reduction methods for logistic regression via Diaconis-Ylvisaker conjugate prior

This repository is associated with the article "Rigon, Aliverti (2022). Conjugate priors and bias reduction for logistic regression models" and contains code implementing bias-reduction via Diaconis-Ylvisaker conjugate prior and tutorials reproducing the results of the article.

• BIRTHWEIGHT focuses on the birthweight dataset available in the MASS package, and contains code to reproduce results from Section 4.1 of the paper.

• HIGH-DIMENSIONAL-SYNTHETIC focuses on a high-dimensional setting inspired by Sur and Candes (2019, PNAS) and reproduces results from Section 4.2 of the paper and some additional considerations on computational time in settings with large n, large p and correlated design matrix.

• ENDOMETRIAL focuses on the endometrial dataset studied in Heinze and Schemper (2002, Stat. in Med.) and contains code to reproduce results from the Supplementary Materials of the paper.