Suchergebnisse

This thesis represents an original contribution to knowledge on ordinal data, which constitutes the leitmotif of the entire research product. Specifically, a short review of preference data presents the leading distance and correlations measures with a focus on two weighted measures (i.e., distance and correlation). A simulation study investigates the effect of introducing positional weights when preferences are analyzed through distance-based approaches. The weighted correlation coefficient is then used as cost function within an evolutionary algorithm for finding the consensus ranking. Then, we will focus on analyzing ordinal data through probabilistic approaches, presenting a new tree-based model, the Bradley-Terry Regression Trunk model (BTRT). Again, a simulation study is conducted to evaluate the performance of the pruning procedure implemented in the new algorithm. This model is applied on two different datasets: the first is composed of self-reported data by students from the University of Cagliari; the second derives from well-known databases and contains financial information about tax revenues by central governments worldwide and their socio-economic characteristics. The BTRT model is applied to the first dataset to partition students based on their preference rankings about the attributes they expect from an ideal professor. For the second dataset, the goal is to apply the BTRT model for partitioning countries based on the size of their tax revenues and how their socio-economic characteristics influence these revenues. The BTRT model furnishes an easy-to-read partition in the form of a small regression tree, called trunk, able to capture the interactions between covariates that cause the most significant decrease in model deviance. Hence, it finds the best interactions between covariates by simultaneously considering their main effects. Finally, the last chapter shows an advance for the BTRT model by following the Mallows specification of the Bradley-Terry model. The Mallows specification works on rankings instead of paired comparisons. It assumes independence across the ranked objects so that the probability of observing a specific ranking is proportional to the product of the estimated worth parameters for each object. The BTRT model with the Mallows specification is applied to the financial dataset to discover the causal effect between government expenditure and tax revenues.

The importance of person-to-person communication about a certain topic (Word of Mouth) is growing day by day, especially for decision-makers. These phenomena can be directly observed in online social networks. For example, the rise of influencers and social media managers. If more people talk about a specific product, then more people are encouraged to buy it and vice versa. Forby, those people usually leave a review for it. Such a review will directly impact the product, and this effect is amplified proportionally to how much the reviewer is considered to be trustworthy by the potential new customer. Furthermore, considering the negative reporting bias, it is easy to understand how customer satisfaction is of absolute interest for a company (as well as citizens' trust is for a politician). Textual data have then proved extremely useful, but they are complex, as the language is. For that, many approaches focus more on producing well-performing classifiers and ignore the highly complex interpretability of their models. Instead, we propose a framework able to produce a good sentiment classifier with a particular focus on the model interpretability. After analyzing the impact of Word of Mouth on earnings and the related psychological aspects, we propose an algorithm to extract the sentiment from a Natural Language text corpus. The combined approach of Neural Networks, characterized by high predictive power but at the cost of complex interpretation (usually considered as black-boxes), with more straightforward and informative models, allows not only to predict how much a sentence is positive (negative) but also to quantify a sentiment with a numeric value. In fact, the General Sentiment Decomposition (GSD) framework that we propose is based on a combination of Threshold-based Naive Bayes (an improved version of the original algorithm), SentiWordNet (an enriched Lexical Database for Sentiment Analysis tasks), and the Words Embeddings features (a high dimensional representation of words) that directly comes from the usage of Neural Networks. Moreover, using the GSD framework, we assess an objective sentiment scoring that improves the results' interpretation in many fields. For example, it is possible to identify specific critical sectors that require intervention to improve the offered services, find the company's strengths (useful for advertising campaigns), and, if time information is present, analyze trends on macro/micro topics. Besides, we have to consider that NL text data can be associated (or not) with a sentiment label, for example: 'positive' or 'negative'. To support further decision-making, we apply the proposed method to labeled (Booking.com, TripAdvisor.com) and unlabelled (Twitter.com) data, analyzing the sentiment of people who discuss a particular issue. In this way, we identify the aspects perceived as critical by the people concerning the "feedback" they publish on the web and quantify how happy (or not) they are about a specific problem. In particular, for Booking.com and TripAdvisor.com, we focus on customer satisfaction, whilst for Twitter.com, the main topic is climate change.

The open-source programming language R has gained a central place in the hydrological sciences over the last decade, driven by the availability of diverse hydro-meteorological data archives and the development of open-source computational tools. The growth of R's usage in hydrology is reflected in the number of newly published hydrological packages, the strengthening of online user communities, and the popularity of training courses and events. In this paper, we explore the benefits and advantages of R's usage in hydrology, such as the democratization of data science and numerical literacy, the enhancement of reproducible research and open science, the access to statistical tools, the ease of connecting R to and from other languages, and the support provided by a growing community. This paper provides an overview of a typical hydrological workflow based on reproducible principles and packages for retrieval of hydro-meteorological data, spatial analysis, hydrological modelling, statistics, and the design of static and dynamic visualizations and documents. We discuss some of the challenges that arise when using R in hydrology and useful tools to overcome them, including the use of hydrological libraries, documentation, and vignettes (long-form guides that illustrate how to use packages); the role of integrated development environments (IDEs); and the challenges of big data and parallel computing in hydrology. Lastly, this paper provides a roadmap for R's future within hydrology, with R packages as a driver of progress in the hydrological sciences, application programming interfaces (APIs) providing new avenues for data acquisition and provision, enhanced teaching of hydrology in R, and the continued growth of the community via short courses and events.

In this study, we propose a time-dependent Susceptible-Exposed-Infected-Recovered (SEIR) model for the analysis of the SARS-CoV-2 epidemic outbreak in three different countries, the United States of America, Italy and Iceland using public data inherent the numbers of the epidemic wave. Since several types and grades of actions were adopted by the governments, including travel restrictions, social distancing, or limitation of movement, we want to investigate how these measures can affect the epidemic curve of the infectious population. The parameters of interest for the SEIR model were estimated employing a composite likelihood approach. Moreover, standard errors have been corrected for temporal dependence. The adoption of restrictive measures results in flatten epidemic curves, and the future evolution indicated a decrease in the number of cases.

In this work we tried to measure the quality of the universities in terms of employment outcomes of their graduates. In addition to previous findings in the literature, we are interested in assessing whether the research activities of universities and/or departments have any bearing on the outcomes of the teaching activities. If universities are conceived as multi product organizations, oriented to both teaching and research, the question naturally arises whether there i complementarity or substitutability between the twos. Among the problems we face, there are the appropriate measure of employability of graduates, the appropriate measure of research activity, the correct unit of analysis (universities or school/departments) and the correct methods for assessing university quality

With the increase of social media usage, a huge new source of data has become available. Despite the enthusiasm linked to this revolution, one of the main outstanding criticisms in using these data is selection bias. Indeed, the reference population is unknown. Nevertheless, many studies show evidence that these data constitute a valuable source because they are more timely and possess higher space granularity. We propose to adjust statistics based on Twitter data by anchoring them to reliable official statistics through a weighted, space-time, small area estimation model. As a by-product, the proposed method also stabilizes the social media indicators, which is a welcome property required for official statistics. The method can be adapted anytime official statistics exists at the proper level of granularity and for which social media usage within the population is known. As an example, we adjust a subjective wellbeing indicator of "working conditions" in Italy, and combine it with relevant official statistics. The weights depend on broadband coverage and the Twitter rate at province level, while the analysis is performed at regional level. The resulting statistics are then compared with survey statistics on the "quality of job" at macro-economic regional level, showing evidence of similar paths.

Twitter is among the most used online platforms for the political communications, due to the concision of its messages (which is particularly suitable for political slogans) and the quick diffusion of messages. Especially when the argument stimulate the emotionality of users, the content on Twitter is shared with extreme speed and thus studying the tweet sentiment if of utmost importance to predict the evolution of the discussions and the register of the relative narratives. In this article, we present a model able to reproduce the dynamics of the sentiments of tweets related to specific topics and periods and to provide a prediction of the sentiment of the future posts based on the observed past. The model is a recent variant of the Polya urn, introduced and studied in Aletti and Crimaldi (2019, 2020), which is characterized by a "local" reinforcement, i.e. a reinforcement mechanism mainly based on the most recent observations, and by a random persistent fluctuation of the predictive mean. In particular, this latter feature is capable of capturing the trend fluctuations in the sentiment curve. While the proposed model is extremely general and may be also employed in other contexts, it has been tested on several Twitter data sets and demonstrated greater performances compared to the standard Polya urn model. Moreover, the different performances on different data sets highlight different emotional sensitivities respect to a public event.

There is an increasing scientific interest in automatically analysing and understanding human behavior, with particular reference to the evolution of facial expressions and the recognition of the corresponding emotions. In this project we propose a technique based on Functional ANOVA to extract significant patterns of face muscles movements, in order to identify the emotions expressed by actors in recorded videos. This research is part of the BIGMATH project, a European Industrial Doctorate funded by the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No 812912.

Pharmacological and non-pharmacological measures will overlap for a period after the onset of the pandemic, playing a strong role in virus containment. We explored which factors influence the likelihood to adopt two different preventive measures against the COVID-19 pandemic. An online snowball sampling (May–June 2020) collected a total of 448 questionnaires in Italy. A Bayesian bivariate Gaussian regression model jointly investigated the willingness to get vaccinated against COVID-19 and to download the national contact tracing app. A mixed-effects cumulative logistic model explored which factors affected the motivation to adopt one of the two preventive measures. Despite both COVID-19 vaccines and tracing apps being indispensable tools to contain the spread of SARS-CoV-2, our results suggest that adherence to the vaccine or to the national contact tracing app is not predicted by the same factors. Therefore, public communication on these measures needs to take in consideration not only the perceived risk associated with COVID-19, but also the trust people place in politics and science, their concerns and doubts about vaccinations, and their employment status. Further, the results suggest that the motivation to comply with these measurements was predominantly to protect others rather than self-protection.

The identification of well-being measures is a matter that has been facing for a long time. In the past, the most commonly employed index was the Gross Domestic Product (GDP), but its adequacy has been questioned for quite all the 20th century, concluding that its use can lead to misleading conclusions (Fleurbaey, 2009). To overcome GDP, have been developed alternative approaches that are still nowadays a core topic. Following the capability approach proposed by Sen (2003), the European Commission, European Parliament, Club of Rome, Organization for Economic Co-operation and Development (OECD) and WWF, in 2007 hosted the conference titled "Beyond GDP". In August 2009, the European Commission released its road map, the Communication "GDP and beyond: Measuring progress in a changing world", when the so-called Stiglitz Commission (Stiglitz et al., 2009) suggested to build a complementary statistical system, focused on social well-being and suitable for measuring sustainability. The advice is to develop indicators that are as clear and appealing as GDP, but more inclusive of environmental and social aspects of progress (OECD, 2013). The proposed measure accounts a set of indicators representing both objective and subjective assessment, and including also people's perception of quality of life. Following the Commission's prompts a huge number of well-being indices have been developed, with different structures, considering a great variety of dimensions and for many purposes. International examples are: the Human Development Index, the Better Life Index, the Happy Planet Index, the Canadian Index of Well-being and the Gross National Happiness Index. In the field of subjective well-being, new measures have been recently proposed using big data, Facebook or Twitter data, to predict life satisfaction based on lexical and topical features (Schwartz et al., 2016, Dodds et al., 2011, Abdullah et al., 2015). In Italy, Iacus et al. (2017) developed the Social Well-being Index (SWBI), a multidimensional well-being composite indicator relying on Twitter data but derived from a new human supervised technique of sentiment analysis. All these new proposals try to overcome the lack in official statistics, however, for this kind of measures, the methodological challenges are still many and open. This work proposes a review of the traditional indices used to measure well-being in Italy, introducing their purposes, limitations, and their progressive evolution. The aim is to provide a detailed image, also to policy-makers, describing the accuracy, timeliness and territorial coverage. All these characteristics are a challenge for a good planning, for a correct international comparison or to integrate well-being indices with new data, represented, as example, by big data.

This thesis is devoted to the development of new hierarchical latent variable models for Dimensionality Reduction with a specific focus on the construction of Composite Indicators (CIs). Since our society is producing a huge quantity of data, the construction of model-based CIs represents an interesting and still open methodological challenge. This dissertation is motivated by the necessity to provide model-based CIs which are built according to a statistical approach avoiding subjective choices (e.g., normative weights), therefore, the new insights and proposals hope to represent a contribution to the current literature. This thesis provides an introduction to CIs, a brief review of the most used methods in Multidimensional Data Analysis framework, a discussion about measurement models and methodological proposals to model latent concepts. Factor Analysis and its hierarchical extensions have been introduced in order to set the starting point of the analysis. A first proposal represents a new latent factor model that could be used for building CIs, it aims to investigate the hierarchical structure of the data in order to define two levels of CIs. The model, named Hierarchical Disjoint Non-Negative Factor Analysis is composed of two novelties: a model which is the two level hierarchical extension of FA and its disjoint extension with non-negative loadings. The latter model is enriched by considerations about the CIs used for tracking coherent policy conclusions. A set of features, properties and rules useful to build "good" CIs have been presented and explained. The last proposal in the thesis represents a new model for positive data correlation matrices which aims to detect reliable concepts and to build the hierarchy from them to the most general one. The proposed models are illustrated both via simulation studies and real data applications, to analyze their performances and abilities. In particular, the main application in this thesis regards the construction of a hierarchically aggregated index for the multidimensional phenomenon Waste Management in European Union. Waste Management is becoming even more important for its impact on human-being's lives, and many data have been produced about it, therefore the construction of a CI able to reduce its dimensionality and to highlight the main dimensions of it has a extraordinary usefulness in order to provide support to EU countries' action and policies.

Nel presente lavoro ci occupiamo di classificare dati misti utilizzando unapproccio gerarchico basato sulla Forward Search. L'individuazione dei gruppi sibasa sull'identificazione di traiettorie simili e poi collegate a mappe bidimension-ali molto ituitive. L'algoritmo proposto pu implementare diverse misure per datimisti, per esempio Gower o Related Mertric Scaling. L'algoritmo sar applicato adati relativi a un ampio insieme di paesi. In economia una idea condivisa che leperformance economiche, ma anche le politiche di sviluppo pi o meno sostenibile,di un paese dipendono dalle loro profonde caratteristiche istituzionali. Tali carat-terisitche sono descritte da indicatori di natura mista. ; In this work we deal with classifying mixed type data using a hierar-chical approach based on Forward Search. The identification of groups is basedon the identification of similar trajectories and then linked to very intuitive two-dimensional maps. The proposed algorithm can implement different measures formixed type data, such as Gower or Related Mertric Scaling. The algorithm will beapplied to data related to a large set of countries. In economics it is a shared idea thata country's economic performance, but also more or less sustainable developmentpolicies depend on their deep institutional characteristics. These characteristics aredescribed by indicators of mixed nature.

The Lancet Countdown is an international collaboration established to provide an independent, global monitoring system dedicated to tracking the emerging health profile of the changing climate. The 2020 report presents 43 indicators across five sections: climate change impacts, exposures, and vulnerabilities; adaptation, planning, and resilience for health; mitigation actions and health co-benefits; economics and finance; and public and political engagement. This report represents the findings and consensus of the 35 leading academic institutions and UN agencies that make up The Lancet Countdown, and draws on the expertise of climate scientists, geographers, engineers, experts in energy, food, and transport, economists, social, and political scientists, data scientists, public health professionals, and doctors.

Inthiswork,weproposeanewprotocolthatintegratesrobustclassificationandvisualiza- tion techniques to analyze mixed data. This protocol is based on the combination of the Forward Search Distance-Based (FS-DB) algorithm (Grané, Salini, and Verdolini 2020) and robust clustering. The resulting groups are visualized via MDS maps and characterized through an analysis of several graphical outputs. The methodology is illustrated on a real dataset related to European COVID-19 numerical health data, as well as the policy and restriction measurements of the 2020–2021 COVID-19 pandemic across the EU Member States. The results show similarities among countries in terms of incidence and the management of the emergency across several waves of the disease. With the proposed methodology, new smart visualization tools for analyzing mixed data are provided.

This paper contributes to the study of environmental and energy policy by using a three-level random intercept (TLRI) model to rank the performance of different countries. Inspired by the literature on Item Response Theory and multilevel latent models, the TLRI model treats policy commitment as a latent variable which is estimated conditional on the difficulty of the policy portfolio implemented by each country. This approach is characterized by three novel aspects. First, the model results in a ranking of countries which is conditional on the complexity of their chosen policy portfolio. Second, it provides a unified framework in which to construct a policy indicator and to study its determinants through a latent regression approach. The resulting country ranking can thus be cleaned from the effect of economic and institutional observables which affect policy design and implementation. Third, the model estimates parameters which can be used to describe and compare policy portfolios across countries. We apply this methodology to the case of energy efficiency policies in the industrial sectors of 29 EU countries between 2004 and 2011. In the conclusions we highlight the future possible applications of this approach, which are not confined to the realm of environmental and energy policy.