Errata and Corrigenda: Bayesian Relative Importance Analysis of Logistic Regression Models: Journal of Statistics Applications & Probability Letters (Vol. 3 (2016):53)

Abstract

The research paper “Bayesian relative importance analysis of logistic regression models” in Journal of Statistics Applications & Probability Letters (Vol. 3 (2016):53-69) extended the relative importance research question in the Wang et al. (2013) from the ordinary linear regression model to the logistic regression model applying the Bayesian approach with different likelihood functions, prior distributions and posterior distributions. The numerical example and simulation studies were all performed on the logistics regression base. The Wang et al (2013) and Wang (2016) are truly independent research about the predictors’ relative importance conducted in the Bayesian framework, and the previous paper was cited.