Varying effects of risk factors on economic losses from fishing vessel accidents: A Bayesian random-parameter quantile regression with heterogeneity in means

作者：Yun Ye, Pengjun Zheng, Pengpeng Xu, Qiaoqiao Ren, Ran Yan, Xiaowei Gao · 发表于：Reliability Engineering & System Safety · 年份：2025 · DOI：10.1016/j.ress.2025.111690 · 被引用次数：4 · 研究领域：Risk and Safety Analysis、Maritime Navigation and Safety、Occupational Health and Safety Research

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 contextu...