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In the model-based clustering literature, we find several methodologies to study latent structures underlying the data, among which mixtures of factor analyzers. However, none of them can detect hierarchical relationships among latent variables. The Ultrametric Gaussian Mixture Model (UGMM) is intended to reach this goal by identifying a hierarchy of variables, starting by partitioning the variables into a reduced number of groups per mixture component. Nonetheless, up to now, it requires complete observations, which is often not the case in real data collections. In this paper, we propose the extension of UGMM in the missing data framework. The proposal is applied to a real data set for inspecting the relationships among features of songs of different genres.

Road accidents represent a concern for modern societies, especially in poor and developing countries. In this paper, we develop a road safety model assuming that the car crashes recorded in Milan (Italy) during 2019 can be appropriately modelled as a realisation of a spatio-temporal point process on a linear network. We adopt a separable first-order intensity function with spatial and temporal components. The temporal dimension is estimated semi-parametrically using an additive Poisson regression model. The spatial dimension is estimated semi-parametrically considering a b-spline transformation of two potentially relevant space-varying covariates, namely the traffic flows and the distance to the closest road sign. This approach permits us to analyse traffic accidents at a very granular spatial scale, hence avoiding potential biases due to data aggregation.

Despite the existence of a normative space as the European Union (EU), the rates of recognition for asylum seekers’ protection statuses remarkably vary across different countries (Van Wolleghem, Sicakkan, 2022). Several studies have attempted to examine the potential explanations of these different between-countries rates of recognition, considering both economics and political reasons, without finding any clear driver (Toshkov, 2014), up to the point that it has been commented that «asylum decisions in Western Europe are highly arbitrary» (Bronkhorst, 1991, p.151). At the same time, it has been argued that changes in asylum policies within a given country may be associated with changes in recognition rates on asylum application flows (Thielemann, 2003), modifying the level of deterrence characterizing a determined socio-economic context (Holzer et al., 2000). On December 3rd 2018, the Italian government approved the so-called “Immigration and security” decree. Among its intentions, regarding a wide range of matters (contrast to mafia and terrorism, urban security), the decree remarkably changed the regulation of asylum, immigration and citizenship (De Petris, 2019). This contribution aims to evaluate the association between the “Immigration and security” decree and recognition rates of asylum applicants in Italy and if and how this association has been conditional on the applicant’s socio-demographic characteristics. This goal will be accomplished by performing some statistical analyses on a dataset created from the annual Eurostat database on the first instances decisions on applications.

During recent years, the concept of sustainability has been spread like one of the most cited and interesting trend topics giving a lot of definitions and good practices to perceive it. One of the aspects less frequently faced is the perception of sustainability for the population and the companies. In this paper, this issue has been analysed using results from a survey in which a sample of 1,000 respondents answered to the perception of sustainability for Italian companies. Since items in the questionnaire also asked for the behaviour of the respondent regards to the company declaring to be sustainable in terms of preferences and availability to spend, it is possible to identify different behaviours from the respondents. From a methodological point of view, these behaviours have been achieved creating some decision trees where each node represents a different group of respondents.