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Road casualties represent the leading cause of death among young people worldwide, especially in poor and developing countries. This paper introduces a Bayesian hierarchical model to analyse car accidents on a network lattice that takes into account measurement error in spatial covariates. We exemplified the proposed approach analysing all car crashes that occurred in the road network of Leeds (UK) from 2011 to 2019. Our results show that omitting measurement error considerably worsens the fit of the model and attenuates the effects of spatial covariates.

High-income inequality, accompanied by substantial regional differentiation, is still a great challenge for social policymakers in many European countries. One of the important elements of this phenomenon is inequality between income distributions of men and women. Using data coming from EU-SILC 2018, we compare the distribution of income for Italy and Poland, and analyze gender gap in these countries. We are interested here to uncover the socioeconomic factors that could contribute to explain the differences observed in the income distribution for men and women.

We investigate here the stability of the obtained results of a variable selection method recently introduced in the literature, and embedded into a model-based classification framework. It is applied to chemometric data, with the purpose of selecting a few wavenumbers (of the order of tens) among the thousands measured ones, to build a (robust) decision rule for classification. The robust nature of the method safeguards it from potential label noise and outliers, which are particularly dangerous in the field of food-authenticity studies. As a by-product of the learning process, samples are grouped into similar classes, and anomalous samples are also singled out. Our first results show that there is some variability around a common pattern in the obtained selection.

Depending on the selected hyper-parameters, cluster weighted modeling may produce a set of diverse solutions. Particularly, the user can manually specify the number of mixture components, the degree of heteroscedasticity of the clusters in the explanatory variables and of the errors around the regression lines. In addition, when performing robust inference, the level of impartial trimming enforced in the estimation needs to be selected. This flexibility gives rise to a variety of “legitimate” solutions. To mitigate the problem of model selection, we propose a two stage monitoring procedure to identify a set of “good models”. An application to the benchmark tone perception data showcases the benefits of the approach.

The difference between females’ and males’ income is one of the main topics in the analysis of gender gap, as it is known that, even with a higher educational level, females earn less than males do. To inspect this, we analyse and estimate the distribution of the ratio of females’ income over males’ income using the methodology based on the distribution of the ratio of two Dagum with three parameters. We applied this method to the Bank of Italy Survey on Household Income and Wealth (SHIW) data to evaluate the deciles, the density functions, and the cumulative distribution functions of the ratio of the females’ income over males’ income in different age classes, Italian areas, and years.