Modelling Skewed and Heavy-Tailed Errors in Bayesian Mediation Analysis

TL;DR

This paper introduces a novel Bayesian mediation analysis method that models skewed and heavy-tailed errors using the Centred Two-Piece Student t Distribution, improving power and robustness over traditional methods.

Contribution

It develops a new flexible distribution for error modeling in Bayesian mediation, with theoretical properties and an R package implementation.

Findings

Enhanced parameter recovery accuracy in simulations

Greater statistical power in mediation testing

Robustness against model misspecification

Abstract

Traditional mediation models in both the frequentist and Bayesian frameworks typically assume normality of the error terms. Violations of this assumption can impair the estimation and hypothesis testing of the mediation effect in conventional approaches. This study addresses the non-normality issue by explicitly modelling skewed and heavy-tailed error terms within the Bayesian mediation framework. Building on the work of Fern\'andez and Steel (1998), this study introduces a novel family of distributions, termed the Centred Two-Piece Student $t$ Distribution (CTPT). The new distribution incorporates a skewness parameter into the Student t distribution and centres it to have a mean of zero, enabling flexible modelling of error terms in Bayesian regression and mediation analysis. A class of standard improper priors is employed, and conditions for the existence of the posterior distribution and posterior moments are established, while enabling inference on both skewness and tail parameters. Simulation studies are conducted to examine parameter recovery accuracy and statistical power in testing mediation effects. Compared to traditional Bayesian and frequentist methods, particularly bootstrap-based approaches, our method gives greater statistical power when correctly specified, while maintaining robustness against model misspecification. The application of the proposed approach is illustrated through real data analysis. Additionally, we have developed an R package FlexBayesMed to implement our methods in linear regression and mediation analysis, available at https://github.com/Zongyu-Li/FlexBayesMed.