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• W2789056699 abstract "Today, aviation is immersed in a shift from old-fashioned reactive and compliance-based safety approaches towards proactive and performance-based methods and tools. Stakeholders have to monitor, gather and analyse safety-related data and information in order to anticipate and predict actual and emerging safety risks. In this context safety analytics and statistics need to evolve to forecast future safety performances and risks. This research adopts an innovative statistical approach involving the use of Bayesian inference and Hierarchical structures to develop statistical estimation and prediction models with different complexities and objectives. The study develops and analyses five Bayesian models of increasing difficulty, two basic and three Hierarchical models, which allows us to explore safety incident data, efficiently identify anomalies, assess the level of risk, define an objective framework for comparing air carriers, and finally predict and anticipate incidents." @default.
• W2789056699 created "2018-03-06" @default.
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• W2789056699 date "2018-04-01" @default.
• W2789056699 modified "2023-10-16" @default.
• W2789056699 title "Prediction of aircraft safety incidents using Bayesian inference and hierarchical structures" @default.
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• W2789056699 doi "https://doi.org/10.1016/j.ssci.2018.01.008" @default.
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