Inference on Reported Vehicular Fatal Accidents in Nigeria Using a Bayesian Model

Autor:	Mbata Ahamefula Ugochukwu, Amusan Ajitoni Simeon, Olalude Gbenga Adelekan
Rok vydání:	2021
Předmět:	business.industry Computer science Inference Artificial intelligence business Machine learning computer.software_genre Bayesian inference computer
Zdroj:	Journal of Mathematics and Statistics Studies. 2:53-61
ISSN:	2709-4200
DOI:	10.32996/jmss.2021.2.1.6
Popis:	The study introduced a special case of the Poisson-Generalized Gamma empirical Bayes model to survey states in Nigeria with a higher risk of fatal accidents. Monte Carlo error and stationary dynamic trace plots were used to validate model convergence and accuracy of the posterior estimates. The main results included the disease mappings that revealed Ebonyi had the highest risk of road vehicular fatal accidents in Nigeria with a relative risk estimate of 1.4120 while Abuja had the lowest risk with a relative risk estimate 0.5711. In terms of geopolitical region, the risk of road vehicular fatal accident is highest in South-South region with a relative risk estimate of 1.1850 while North-Central had the lowest risk with a relative risk estimate of 0.7846. The study is to aid planned government programs to ameliorate vehicular road carnage in Nigeria.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::ff4793474c1b597c8754460fe9225b36 https://doi.org/10.32996/jmss.2021.2.1.6 Zobrazit plný text záznamu