A Comprehensive Approach through Robust Regression and Gaussian/Mixed-Markov Graphical Models on the Example of Maritime Transportation Accidents: Evidence from a Listed-in-NYSE Shipping Company

Abstract

The main objective of this article is to determine the internal factors of maritime transportation accidents using a comprehensive approach through robust regression and Gaussian/mixed-Markov graphical models. Globally, this could be a strong incentive for the employees to negotiate higher compensation and for the insurance companies to impose higher premiums to cover the risk for these kinds of accidents. The article uses a dataset consisting of 166 real cases (human injuries) in the period 2014–2022 in different ships owned by a shipping company indexed in the New York Stock Exchange. The results of the study support the hypotheses as have been set in the article, connecting the internal factors with the injuries of any type. The practical implementation of the study is its ability to be used by policy makers in shipping to compensate employees depending on the risk of their work on board and at the same time to calculate the insurance premiums in a more accurate way. The originality of the research lies in the fact that this is a unique study in maritime transportation related to human accidents and not on ship or cargo casualties. The idea came from the results of another study conducted on a bibliometric analysis of the factors related to maritime transportation accidents. The findings of the current study can provide valuable insights to stakeholders and shipping planners in formulating effective policies for better wage packages and insurance premiums.