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Understanding the determinants of economic loss in fishing vessel accidents is crucial for maritime risk assessment and policy development. This study proposes a Bayesian Random-Parameter Quantile Regression with Heterogeneity in Means (BRPQRHM) framework, and compares it with the Bayesian fixed-parameter regression (BFPR), Bayesian fixed-parameter quantile regression (BFPQR), and Bayesian random-parameter quantile regression (BRPQR) to investigate the varying and heterogeneous effects of vessel, environment, and accident-related factors on economic loss. The proposed approach addresses key limitations of conventional models by offering three major advantages by enabling a richer characterization of covariate effects across quantiles, improving robustness to outliers in heavy-tailed and skewed data, and accounting for unobserved heterogeneity through random parameters influenced by covariates. Using a dataset of fishing vessel accidents in Ningbo waters, the results demonstrate substantial variations in covariate effects across quantiles and highlight the superiority of quantile regression in modeling the skewed and heavy-tailed distribution of economic losses. The BRPQR and BRPQRHM models significantly improve model fit at higher quantiles and reveal that the effects of variables such as human errors and crew qualifications are probabilistic rather than fixed. In particular, the BRPQRHM model at the 98% quantile captures complex interactions between crew effects and contextual factors, including vessel width, visibility, and accident type. These findings underscore the importance of accounting for the unobserved heterogeneity and provide novel insights into the risk factors associated with severe fishing vessel accidents.
Ye et al. (Sun,) studied this question.