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The book provides new developments in data analysis and statistical multivariate methods, computational statistics and algorithms, including new topics which are of central interest to modern statistics. The reader will find advanced methodologies and computational methods which are very helpful to analyze real phenomena characterized by large data bases. Furthermore, the volume includes papers devoted to original and innovative applications of recent statistical theory and complex approaches of statistical data analysis.

In molte applicazioni l'ipotesi dell'esistenza di un concetto generale (un fenomeno multidimensionale), definito mediante concetti più specifici, è spesso avvalorata. In letteratura, molteplici metodologie di tipo sequenziale sono state proposte con lo scopo di identificare una gerarchica di dimensioni latenti. In questo articolo indaghiamo il fenomeno del consumo di droghe mediante una matrice di correlazione ultrametrica, che permette di individuare diversi, disgiunti gruppi di droghe e le loro relazioni gerarchiche, a partire dalla matrice di correlazione dei dati osservati. Data la sua rilevanza sociale ed economica, un approccio basato su modello per lo studio del consumo di droghe può fornire una conoscenza più approfondita di tale fenomeno, che a sua volta può risultare fondamentale nella definizione di politiche volte alla sua riduzione. ; In many real applications, the existence of a general concept (a multidimensional phenomenon) composed of nested specific ones is often theorised. In the specialised literature, different sequential methodologies have been proposed to identify a hierarchy of latent dimensions. In this paper, we investigate drug consumption via an ultrametric correlation matrix which allows to detect different, nonoverlapping groups of drugs and their hierarchical relationships, starting from the correlation matrix of the observed data. Since its social and economic relevance, a model-based approach to drug consumption can provide an in-depth understanding of this challenging phenomenon, which turns out to be fundamental to address policies aimed at reducing it.

This book addresses a wide range of recent methodological aspects, applications and best practices of statistics production. It comprises a selection of peer-reviewed contributions of methodological and applied interest presented at the 4th Conference of European Statistics Stakeholders, CESS 2022, held in Rome, Italy, on October 20-21, 2022. The first part discusses statistical methods with applications to environmental risk assessment, sentinels data, surveillance systems during the Covid-19 pandemic, healthcare risk management, the analysis of regional or structural changes of scale, household distributional accounts, regional rental prices on municipalities, the network topology of the Euro area interbank market, tourism statistics and big data, statistical literacy, and Sustainable Development Goal composite indicators for EU countries. The second part focuses on statistical methodologies for complex data analysis, namely the optimal number of clusters to rank a model-based index, clustering methods for asymmetric data using spectral approaches, a family of parsimonious matrix-variate mixture models for heavy-tailed data, the importance of robust second-stage regressions for financial data, and on perturbation methods. In view of the overarching theme "The European Data Ecosystem for the Statistical Information of the Digital Age" and the importance of statistical data for monitoring the progress of the United Nations' Sustainable Development Goals, the CESS 2022 meeting provided a forum for discussion on methodologies, results, challenges and best practices among methodologists, producers, and users of European Statistics from academia, the national statistical offices and the institutions of the European Union. Chapter Estimating regional rental prices on LAU 2 municipalities in North Rhine-Westphalia is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

AbstractIn the analysis of the difference in the distribution and profiles of the equitable and sustainable well-being, the territorial dimension is a fundamental reading-key for local policies since it allows the areas of advantage or relative deprivation to emerge more accurately. Specifically, in Italy the provincial level coincides with the administrative area of metropolitan cities, which are the subject of growing attention from European and national policies. The BES 2018 report by Italian National Institute of Statistics (ISTAT) has confirmed that from 2015 an improvement in many areas of well-being has been marked, even if territorial differences remain stable both in levels and dynamics. These differences appear in some cases as real structural differences between the North and South of Italy. Then, the measures of equitable and sustainable well-being in the territories allow, in various degrees, to deepen and specify this situation employing synthetic measures of well-being. In this work, we propose a statistical methodology focused on the simultaneous partial least squares structural equation modeling and simultaneous K-means clustering to obtain a composite indicator of Italian well-being and at the same time a classification of Italian territorial micro-areas by means of the just updated provincial data about BES 2018. In this way, the territorial differences of well-being can be more reliably and more exactly defined on the basis of the relationships among all elementary indicators and domains proposed in the analysis of well-being by ISTAT.

Integration is a multidimensional process, which can take place in different ways and at different times in relation to each of the single economic, social, cultural, and political dimensions. Hence, examining every single dimension is important as well as building composite indexes simultaneously inclusive of all dimensions in order to obtain a complete description of a complex phenomenon and to convey a coherent set of information. In this paper, we aim at building an immigrant integration composite indicator (IICI), able to measure the different aspects related to integration such as employment, education, social inclusion, active citizenship, and on the basis of which to simultaneously classify territorial areas such as European regions. For this application, the data collected in 274 European regions from the European Social Survey (ESS), Round 8, on immigration have been used.