From Marginals to Array Structure with the Shuttle Algorithm

In many statistical problems there is the need to analyze the structure of an unknown n-dimensional array given its marginal distributions. The usual method utilized to solve the problem is linear programming, which involves a large amount of computational time when the original array is large. Alternative solutions have been proposed in the literature, especially to find less time consuming algorithms. One of these is the shuttle algorithm introduced by Buzzigoli and Giusti [1] to calculate lower and upper bounds of the elements of an n-way array, starting from the complete set of its (n-1)-way marginals. The proposed algorithm, very easy to implement with a matrix language, shows interesting properties and possibilities of application. The paper presents the algorithm, analyses its properties and describes its disadvantages. It also suggests possible applications in some statistical fields and, in particular, in Symbolic Data Analysis and, finally, shows the results of some simulations on randomly generated arrays

From Marginals to Array Structure with the Shuttle Algorithm

In many statistical problems there is the need to analyze the structure of an unknown n-dimensional array given its marginal distributions. The usual method utilized to solve the problem is linear programming, which involves a large amount of computational time when the original array is large. Alternative solutions have been proposed in the literature, especially to find less time consuming algorithms. One of these is the shuttle algorithm introduced by Buzzigoli and Giusti [1] to calculate lower and upper bounds of the elements of an n-way array, starting from the complete set of its (n-1)-way marginals. The proposed algorithm, very easy to implement with a matrix language, shows interesting properties and possibilities of application. The paper presents the algorithm, analyses its properties and describes its disadvantages. It also suggests possible applications in some statistical fields and, in particular, in Symbolic Data Analysis and, finally, shows the results of some simulations on randomly generated arrays

Chapter Big data analysis and labour market: an analysis of Italian online job vacancies data

Economists and social scientists are increasingly making use of web data to address socio-economic issues and to integrate existing sources of information. The data produced by online platforms and websites could produce a lot of useful and multidimensional information with a variety of potential applications in socio-economic analysis. In this respect, with the internet growth and knowledge, many aspects of job search have been transformed due to the availability of online tools for job searching, candidate searching and job matching. In European countries there is growing interest in designing and implementing real labour market information system applications for internet labour market data in order to support policy design and evaluation through evidence-based decision-making. The analysis of labour market web data could provide useful information for policy-makers to define labour market strategies as big data, jointly with official statistics, support policy makers in a pressing policy question namely “How to tackle the mismatch between jobs and skills?”. In this regard, the topic of skills gap, how to measure it and how to bridge it with education and continuous training have been tackled by using the big data collection, such as the Cedefop (European Center for the Development of Vocational Training) initiative and the Wollybi Project (made by Burning Glass). In this framework, this contribution focuses on the issues arising from the use (and the usefulness) of on-line job vacancy data to analyse the Italian labour market by using the Wollybi data available for the years 2019 and 2020. Furthermore, the availability of data for the year 2020, will allow us to evaluate whether there has been an impact of COVID19 in terms of needed skills and required occupations in the online job vacancies

Chapter A statistical information system in support of job policies orientation

A significant problem for labour market policies relies on the individuation of the most advisable skills to have and to enhance through focused training offers. Vocational training systems and institutions are called to answer the question posed by every person looking for a new job or professional opportunities: which are the skills-to-have to enhance the professional profile? Many efforts have been made to answer this question, mainly designing predictive models; however, these models are often limited to specific economic sectors and usually don’t adopt a country-specific perspective. This paper proposes a recommendation system oriented to specific users: once that the user has described his/her skills profile, the system suggests the skills that, once got, will fit with the most frequent job vacancies. In this proposal perspective, the skills are proposed regardless of the economic sector, and they are compatible with the characteristics of the specific country labour market. In this contribution, we will focus on the Italian market; the recommendation system is based on the job ads published by Italian companies on various websites for both 2019 and 2020 after the skills required for each job offer have been mapped to one of the skills presented in the classification of European Skills/ competence, qualifications ad Occupations (ESCO)

Gut to brain interaction in Autism Spectrum Disorders: A randomized controlled trial on the role of probiotics on clinical, biochemical and neurophysiological parameters

Background: A high prevalence of a variety of gastrointestinal (GI) symptoms is frequently reported in patients with Autism Spectrum Disorders (ASD). The GI disturbances in ASD might be linked to gut dysbiosis representing the observable phenotype of a "gut-brain axis" disruption. The exploitation of strategies which can restore normal gut microbiota and reduce the gut production and absorption of toxins, such as probiotics addition/supplementation in a diet, may represent a non-pharmacological option in the treatment of GI disturbances in ASD. The aim of this randomized controlled trial is to determine the effects of supplementation with a probiotic mixture (Vivomixx®) in ASD children not only on specific GI symptoms, but also on the core deficits of the disorder, on cognitive and language development, and on brain function and connectivity. An ancillary aim is to evaluate possible effects of probiotic supplementation on urinary concentrations of phthalates (chemical pollutants) which have been previously linked to ASD. Methods: A group of 100 preschoolers with ASD will be classified as belonging to a GI group or to a Non-GI (NGI) group on the basis of a symptom severity index specific to GI disorders. In order to obtain four arms, subjects belonging to the two groups (GI and NGI) will be blind randomized 1:1 to regular diet with probiotics or with placebo for 6 months. All participants will be assessed at baseline, after three months and after six months from baseline in order to evaluate the possible changes in: (1) GI symptoms; (2) autism symptoms severity; (3) affective and behavioral comorbid symptoms; (4) plasmatic, urinary and fecal biomarkers related to abnormal intestinal function; (5) neurophysiological patterns. Discussion: The effects of treatments with probiotics on children with ASD need to be evaluated through rigorous controlled trials. Examining the impact of probiotics not only on clinical but also on neurophysiological patterns, the current trial sets out to provide new insights into the gut-brain connection in ASD patients. Moreover, results could add information to the relationship between phthalates levels, clinical features and neurophysiological patterns in ASD. Trial registration: ClinicalTrials.gov Identifier: NCT02708901. Retrospectively registered: March 4, 2016

The Evaluation of University Departments. A Case Study for Firenze

University evaluation, Efficiency analysis, Data envelopment analysis, C10, I00, O50,