Determinants of Gender-based Violence Against Women in Spain: An Asymmetric Bayesian Model.

The number of fatalities in Spain due to gender-based violence has increased in recent years, with a new rise in 2019, reaching the highest figure since 2015, a year that registered a peak with 60 victims. This article analyzes a database obtained from a survey on gender violence conducted by the Spanish Centre for Sociological Research. The survey, prepared by the Government Delegation for Gender Violence, consisted of interviews with women aged over 15 years living in 858 municipalities distributed over 50 provinces in Spain. The data reveal that most of the women interviewed have not suffered any type of physical, sexual, or psychological abuse. Hence, the application of standard logistic methodologies which suppose symmetric responses, can lead to a poor specification of the model, a misinterpretation of marginal effects and unidentified predictors. It seems more appropriate to consider an asymmetric link function to explain the probability of abuse (physical, sexual, or psychological). The Bayesian methodology allows the incorporation of such an asymmetric function improving the specification of the model. In this article, we compare both methodologies and prove that Bayesian asymmetric performs better results by considering several diagnostic criteria. Furthermore, this methodology detects some significative factors that are not revealed by the classical method, e.g., the partner's nationality for sexual abuse or the women's total number of intimate partners for psychological abuse. Bayesian asymmetric estimations reveal no significance concerning to the lowest partner's level of education for physical abuse but if the intimate partner is currently studying this reduces the probability of sexual abuse. The woman's level of education is not relevant to the physical, sexual, or psychological abuses suffered. Therefore, the findings may help identify economic and sociological factors not previously considered in this area and highlight policies that may be adopted or revised to help overcome this social problem.

Modelling road accident blackspots data with the discrete generalized Pareto distribution.

This study shows how road traffic networks events, in particular road accidents on blackspots, can be modelled with simple probabilistic distributions. We considered the number of crashes and the number of fatalities on Spanish blackspots in the period 2003-2007, from Spanish General Directorate of Traffic (DGT). We modelled those datasets, respectively, with the discrete generalized Pareto distribution (a discrete parametric model with three parameters) and with the discrete Lomax distribution (a discrete parametric model with two parameters, and particular case of the previous model). For that, we analyzed the basic properties of both parametric models: cumulative distribution, survival, probability mass, quantile and hazard functions, genesis and rth-order moments; applied two estimation methods of their parameters: the µ and (µ+1) frequency method and the maximum likelihood method; used two goodness-of-fit tests: Chi-square test and discrete Kolmogorov-Smirnov test based on bootstrap resampling; and compared them with the classical negative binomial distribution in terms of absolute probabilities and in models including covariates. We found that those probabilistic models can be useful to describe the road accident blackspots datasets analyzed.