Abstract
The war against drugs started in Mexico in 2007. During the following years, there was an unprecedented increase in violence and homicides across the country. It is important to find vulnerable groups of population and to quantify effects of demographic covariates, and spatial and time dependence. We present a Bayesian Poisson regression model of the number of homicides with four factors: sex, age, year, state of occurrence, and two-way interactions of these variables. For the main effects of sex and age, we define independent prior distributions, a dynamic linear prior for temporal effects, and conditionally autoregressive processes for spatial effects and spatial interactions. Identification of vulnerable groups by regions provides a tool to design prevention policies targeted at local levels.
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Sánchez-Castañeda, M.d.l.D., Nieto-Barajas, L.E., Fuerte Celis, M.d.P. (2022). Bayesian Analysis of Homicide Rates in Mexico from 2000 to 2012. In: Antoniano-Villalobos, I., Fuentes-García, R., Naranjo, L., Nieto-Barajas, L.E., Ruiz-Velasco Acosta, S. (eds) Interdisciplinary Statistics in Mexico. Springer Proceedings in Mathematics & Statistics, vol 397. Springer, Cham. https://doi.org/10.1007/978-3-031-12778-6_13
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