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Assuming a high education level associated with a high probability of job occupancy and greater income, comparative exercises analyzing academic performances and socioeconomic dynamics at regional, country, or supra-national scales have intensified in recent years. As far as tertiary education is concerned, a great disparity in academic performance was characteristic of OECD countries. While adults 25–34 years old were attaining tertiary degrees more frequently than adults 55–64 years old, adults 30–34 years old with at least one tertiary-educated parent were more likely to attain a tertiary degree than individuals from families whose parents have attended secondary—or at least primary—education. ‘Mobility’ and ‘opportunities’ are two dimensions of sustainable education systems that deserve further investigation when assessing disparities in academic performances as a possible source of unsustainable development and social polarizations. ‘Mobility’ refers to the probability of achieving tertiary education for children coming from families with a different—i.e., lower (e.g., secondary or primary)—level of education. ‘Opportunities’ refers to the probability for a child to attain tertiary education, regardless of the education level achieved by the parents. The present study proposes a quantitative assessment of both dimensions through an original approach and novel statistical measures ranking OECD countries. A comparison of individual rankings of ‘mobility’ and ‘opportunities’ reveals counterintuitive results in some cases. To overcome this issue, our study introduces aggregate methods combining the two measures with the aim of developing a bivariate ranking that accounts for both dimensions simultaneously and delineates a more complete evolution of academic performance divides in advanced economies.

The SWELTO project is at present entirely supported by the INAF-Turin Astrophysical observatory. The SuperSID monitorwas provided as in kind contribution by University of Stanford, whose support is gratefully acknowledged., SWELTO - Space WEather Laboratory in Turin Observatory is a conceptual framework where new ideas for the analysis of space-based and ground-based data are developed and tested. The input data are (but not limited to) remote sensing observations (EUV images of the solar disk, Visible Light coronagraphic images, radio dynamic spectra, etc...), in situ plasma measurements (interplanetary plasma density, velocity, magnetic field, etc...), as well as measurements acquired by local sensors and detectors (radio antenna, fluxgate magnetometer, full-sky cameras, located in OATo). The output products are automatic identification, tracking, and monitoring of solar stationary and dynamic features near the Sun (coronal holes, active regions, coronal mass ejections, etc...), and in the interplanetary medium (shocks, plasmoids, corotating interaction regions, etc...), as well as reconstructions of the interplanetary medium where solar disturbances may propagate from the Sun to the Earth and beyond. These are based both on empirical models and numerical MHD simulations. The aim of SWELTO is not only to test new data analysis methods for future application for Space Weather monitoring and prediction purposes, but also to procure, test and deploy new ground-based instrumentation to monitor the ionospheric and geomagnetic responses to solar activity. Moreover, people involved in SWELTO are active in outreach to disseminate the topics related with Space Weather to students and the general public.

In this paper we focus on the initiation option featured in many Guaranteed Lifelong Withdrawal Benefit variable annuity contracts, granting their owner the right to decide the age at which lifetime withdrawals should begin. Such contracts have been successfully analysed using a PDE approach. The latter method is elegant, but becomes less viable when the valuation model is more involved and other guarantees are considered. As an alternative, we exploit the Least Squares Monte Carlo approach and model the interaction of the initiation option with lapses and other riders.

Spatially unequal demographic dynamics lead to a progressive fragility of a territory and its socio-economic system. In Italy, municipalities in demographic malaise tend to be increasingly small in size and peripheral in location, and their local spatial aggregation increased over time. A spatial approach is here proposed to investigate the dynamics across time and space of the population variations in Italian municipalities. Global and local spatial autocorrelation analysis and several models of regression were run using as study variable the average growth rates at municipality level. The spatial autocorrelation of the study variable is quite high and stable over time. The regression results show that spatial models (SAM and SAR) outperform the non-spatial model (OLS) and that SAR is the best model. The results also underline that the variation of population is significantly affected by its values in the neighbouring municipalities, confirming the spatial nature of the phenomenon. The presence of schools in the municipality emerges as a key factor for the increase/decrease of the population. Moreover, the decomposition of the effects into direct and indirect effects shows that all the independent variables produce their effects almost 70% directly and 30% indirectly.

Fin da prima della sua introduzione nel 2012, la lista delle riviste di “classe A” per le Scienze Statistiche ha generato un ampio dibattito nella comunità scientifica riguardante la sua formulazione. La lista è uno dei prodotti della classificazione delle riviste proposte dall’Anvur (l’Agenzia Nazionale di V alutazione del sistema Universitario e della Ricerca) con il fine principale di calcolare i valori minimi degli indicatori di qualificazione scientifica usati nella procedura relativa all’Abilitazione Scientifica Nazionale (ASN). Il lavoro ha l’obiettivo di analizzare i dati attualmente disponibili sulla produzione degli accademici italiani per verificare se i potenziali cambiamenti osservati possano essere messi in relazione con la composizione della lista che classifica le riviste. Since before its first introduction in 2012, the list of journals of “class A” for Statistical Sciences has generated a large debate in the scientific community on its formulation. The list is one of the outcomes of the ranking of journals proposed by Anvur (the Italian National Agency for the Evaluation of Universities and Research Institutes) with the main purpose of calculating the minimum values of the indicators of scientific qualification used in the National Scientific Habilitation (ASN) procedure. The aim of the paper is to analyze the data currently available on the research outputs of the Italian academic statistical community to check if any of the potentially observed changes may be related to the composition of the rating of journals.

Traditional phase II oncology clinical trials are focussed on a single treatment for a subgroup of patients with specific histological characteristics. Recently, the advances in cancer biology and genomic medicine have led to the flourish of new molecularly targeted therapies and to a re-thinking of the design of phase II trials assessing clinical activity. In this work, we evaluate the potential benefit of information borrowing across tumor subgroups in basket trials by comparing the operating characteristics of the models presented in the literature. In addition, we suggest a modification of the model proposed in [2] which exhibits good performances in terms of both the average number of patients treated and the ability to preserve the type I error rate.

Italy boasts the most heritage sites in the world. The cultural heritage is widespread throughout the Italian peninsula, indeed one out of three municipalities hosts at least one museum or an analogous structure. In this context, it is relevant to identify new city networks in order to promote their museums and their points of attraction. In the paper, geo-referenced socio-demographic data together with a new statistical indicator, obtained through the linear combination of visitors ar- rivals, number of tourist accommodations, indexes of cultural and environmental attractions are used to define various touristic sustainable routes in Lecce district. The final results will help policy makers in planning some possible actions aimed at both the cultural development of the territory and definition of strategies for tourism deseasonalisation.

A study is presented for exploring the possibility of applying the source set approach (Djordjilovic V, Chiogna M.(2018)), developed under the assumption of normality, to count data, after data transformation. Some explanations about the source set approach, data trans- formations and the simulation setting are provided. The suggestion is given that the deviance-based or quantile randomized residuals could provide a better basis for data transformation when coupled with source set analysis, along with standard trasformations such as log transformation or square root transformation.

In this work we propose optimally weighted L2 distances for spatially dependent functional data. Two different spatial structures have been considered: a classical georeferenced spatial structure and a connected network one. In these two situations, assuming a penalized basis representation for the functional data, we consider weight functions depending on the spatial location. Real metereological data have been analyzed in order to show performances of the proposed distances.

We propose a tool for exploring the number of clusters based on pivotal methods and consensus clustering. K-means algorithm is used to learn the pairwise similarity via the co-occurrence of points in multiple partitions of the data. This similarity can be used to investigate the number of groups and detect arbitrary shaped clusters. Different criteria for identifying the pivots are discussed, as well as preliminary results concerning the selection of the optimal number of clusters.

There is an increasing interest in researchers on the use of modern sensor networks deployed in smart cities to collect data that can be used for a better man- agement of the transportation systems, and, more generally, to improve its impact on the environment. A fairly recent technology, Distributed Acoustic Sensors is be- ing used more and more in the field of transportation system, especially in the field of traffic management. In this paper we propose a methodology for exploiting the data of moving vehicles, captured through these sensors to detect and classify the types of passing vehicles. The methodology is based on a data processing pipeline, whose purpose is to exploit signal processing algorithms to clean the data and to use a clustering algorithm to detect the types of vehicle. The data used for testing the methodology is collected through a series of experi- ments with the DAS technology, in a real city environment.

In this paper, we propose a new approach for clustering time series showing similar time-varying moments. At this aim, we compute a dissimilarity measure assuming that the estimated conditional moments are continuous functions indexed by time. Conditional moments based clustering allows to obtain different classifications according to the data distribution’s parameters. We show the usefulness of the proposed clustering procedure with an application to the financial time series in the DAX30 index

Toroidal time series are temporal sequences of bivariate angular observations that often arise in environmental and ecological studies. A hidden semi- Markov model is proposed for segmenting these data according to a finite number of latent classes, associated toroidal densities. The model conveniently integrates circular correlation, multimodality and temporal auto-correlation. A computationally efficient EM algorithm is proposed for parameter estimation. The proposal is illustrated on a time series of wind and sea wave directions.

Media are instruments with a high capacity to penetrate inside the sociocultural fabric. In this paper we examine the informative function of media, focusing in particular to its products the news. The aim of this paper is to study the life cycle of a collection of news, inside a defined medial ecosystem. In particular, we analyze two levels of the medial market, the product (the news), and the channel (the sources). The methodology we chosen is the multiple factor analysis (AFM), which allows us to scan multiple data tables that measure sets of variables collected on the same observations.

Social inclusion and integration are prominent topics in social sciences, and specifically in the educational field, in order to contrast the experience of inequality conditions at school. Using data from the 2015 national survey on “Integration of the second generation”, the paper proposes a synthetic indicator of educational integration of second-generation children in Italy. The chosen methodology is suitable to model subjective assessments on rating scales while accounting for both agreement and heterogeneity in response patterns. L’inclusione sociale e l’integrazione rappresentano temi cruciali nelle scienze sociali ed assumono rilievo specifico nell’ambito dell’istruzione. Utilizzando i dati dell’Indagine Istat sull’integrazione delle seconde generazioni (ISG2015), lo studio propone un indicatore sintetico di integrazione scolastica che considera sia la percezione del tratto in esame che l’eterogeneità delle risposte.

Longitudinal data over the past 20 years have seen a greater diffusion in the social sciences. Accompanying this growth was an interest in the methods for analyzing such data. Structural Equation Modeling (SEMs) and especially Partial Least Squares Path Modeling (PLS-PM) are a valuable way to analyze longitudinal data because it is both flexible and useful for answering common research questions. The aim of this paper is to demonstrate how PLS-PM can help us to analyze longitudinal data.

In this paper we propose a cluster-wise regression strategy for aggregated data represented by distributions. The basic idea is that the set of observed data are related by local causal relationships for different clusters. Some cluster-wise regres- sion methods are based on K-means clustering algorithms, where the representa- tives of clusters are expressed by linear models and the assignment of an element to a cluster is performed according to the minimum distance to the model, in the sense of ordinary least squares (OLS). The proposed method extends this strategy to data expressed by empirical distributions or histograms. The present work refers to one of the regression models proposed in the analysis of the dependence of his- togram type variables and to the k-means algorithm developed for such data. The metric used is the L2 Wasserstein distance. An application on real distributional data corroborate the method.

The paper introduces HateViz dashboard, an interactive platform Twitter based about hate speech against women. Starting from texts collected from Twitter, using R packages, the aim of dashboard is to provide to the community of decisionmakers an easy-to-use tool to monitor this important phenomenon. The dashboard mixes three methods: textual mining, latent topic models and textual network analysis and proposes a new approach for explaining the network based on topics and terms. Joint usage of topic modelling and textual network approach results in a better description of semantic content of each topic.

Dyadic data analysis (DDA) is increasingly being used to better under- stand, analyze and model intra- and inter-personal mechanisms in various types of dyads such as husband-wife, caregiver-patient, doctor-patient, parent-child or athlete-coach as in our example. A key strength of the DDA is its flexibility to take the (non)independence available in the dyads into account. In this article, we illustrate the value of using DDA to examine how sports performance is perceived by an athlete and if it is consistent with the declared performance by his/her coach. A probability summary for ordered comparison of groups referred to a measure of stochastic superiority is used to indicate the consistency of perceived assessments.

This research presents a new probabilistic approach for the analysis of preference data when dealing with paired comparisons. The combination of the extended log-linear Bradley-Terry model with the regression trunk methodology generates a new model that allows finding interaction effects between subject-specific covariates. By fitting Poisson regressions to find the best split points and applying the final pruning procedure, the result is a small regression tree (so-called regression trunk). It represents a compromise between an easier interpretation of higher-order interaction effects and an efficient partition of individuals according to their preference scales.

Composite confidence indicators are widely used to nowcast GDP. In this paper, we aim to construct a new composite confidence indicator which weighting scheme reflects the impact of consumer and business confidence on economic conditions. While GDP is quarterly measured, confidence indicators are monthly recorded. Our approach allows us to deal with data sampled at different frequencies. In particular, we propose a weighting scheme estimation based on U-MIDAS regression techniques.

In this paper, we highlight possible gender differences in performance of students attending Italian university STEM degree courses, controlling for sociodemographic and territorial variables.We used the micro-data from the Italian Ministry of Education, Universities and Research database of the 2011-12 freshmen cohort enrolled at an Italian bachelor university degree course. The analysis was dealt with by fitting a discrete-time competing-risks model. Bachelor degree graduation, dropout, and change to non-STEM courses were considered as competing events. The aim is to highlight gender gaps in the student performance, and the STEM courses where this gap is more critical.

The aim of the paper is to propose a simulation study to explore the multicollinearity problem in quantile regression, as compared to the classical linear regression. The simulation exploits the concept of a relevant subspace and relevant predictors, considering different degrees of collinearity among the involved predictors. The approach is based on principal components and consists in evaluating the degree of dependence between the predictors on the basis of the eigenvalue structure of their covariance matrix. It is well known that in case of highly intercorrelated predictors the least squares coefficients, although determinate, posses large standard errors, causing precision problems for their estimation. For this reason, results of the simulation study focus on standard errors estimated according to different collinearity levels. A possible solution based on regression on principal components is briefly presented.