Ricerc@Sapienza

This project deals with data ranking, preference and ordinal data analysis, which is a theme of peculiar relevance in applied sciences. We will provide new methodological devices for treating ranked data and discuss alsotheir applications in important empirical contexts.
Under a methodological perspective, two main directions are explored. By one side, we adopt a model free approach and develop new clustering procedures. The employed techniques include fuzzy clustering, discrete copulas and Bayesian networks; by the other side, we discuss rank-size problems by identifying best fit curves approximating ranked data. Under this perspective, the project aims at developing new fitting law and synthetic indicators for ranked data. The theme of the assessment of the outliers will be also faced with specific attention.

Patr of the project will be devoted to the identification of real data applications of peculiar meaningfulness. Among them, the project aims at analysing the yearly official data at provincial level that Istat produces on the quality of life and socio-economic well-being indicators of the italian population.

The research project is expected to add to the knowledge of the ranking procedures but also to the considered empirical instances. In terms of methods, we aim at creating new versatile instruments that can be suitably adopted in the broad context of statistical analysis of the data. Under the point of view of the application, we pursue the ambitious target of contributing to a deeper understanding of the explored socio-economic phenomena; in so doing, we aim at providing also instruments whose informative content can be of real usefulness for policymakers.