Identifying influential observations in a Bayesian multi-level mediation model.

ABSTRACT: Increasingly complex models are being fit to data these days. This is especially the case for Bayesian modelling making use of Markov chain Monte Carlo methods. Tailored model diagnostics are usually lacking behind. This is also the case for Bayesian mediation models. In this paper, we developed a method for the detection of influential observations for a popular mediation model and its extensions in a Bayesian context. Detection of influential observations is based on the case-deletion principle. Importance sampling with weights which take advantage of the dependence structure in hierarchical models is utilized in order to identify the part of the model which is influenced most. We make use of the variance of log importance sampling weights as the measure of influence. It is demonstrated

SUBMITTER: Vecerova S 

PROVIDER: S-EPMC9041922 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature