Please limit the report to max. 3-5 pages, including tables and
figures and use the following structure as much as possible.
Name and last name
Federica Nicolussi
Project title
Multivariate Measurement of Poverty Through Chain Graphical Models
(MMPTCGM)

Abstract (max 300-500 words)
Poverty is not simply lack of financial resources; a broader more effective definition should naturally involve a multitude of social-economics factors. The mere income distribution of a country in fact results in ignoring social dynamics able to describe and predict poverty. This project focuses on this multidimensional nature of poverty measurement taking into account factors which can be used as indicator of wealth and social inequality. Such factors are usually categorized into categorical variables and the interest is the underlying system of multivariate interdependences.
Marginal Models, as proposed by Bergsma and Rudas, 2002, appears to have the potential to describe dependences/independences more precisely than traditional multivariate tools such as logistic regression.
Moreover, we will focus on Chain Graphical Models (CGM), i.e. chain-graph-based models recently proposed in the literature, which in addition offer a visual interpretation of the problem by means of a graph where variables are represented as vertices and inter-relationships are represented by the presence/absence of edges, as detailed for instance in Lauritzen (1996). CGMs are able to represent complex structures of dependence/independence, taking advantage of the possibility of grouping the variables into components. This allows for easily representing the variables as "purely explicative", "purely response" and "intervening".
With the help of this statistical tools we will test all possible graphical models to
The main objective is about to find the suitable model(s) which best represents the real situation.
The selected model(s) & its graph will allow for analysing in a multivariate context whether and how poverty is affected by the selected set of risk factors.
Introduction and motivation of visit
The main aim of my participation in this project is to deep my understanding of the poverty phenomena within the social and economic reality. Another important goal is to improve my knowledge about statistical models which I deepened in my PhD thesis (Supervisor: Prof. Colombi) and in a successive paper (in collaboration with my Post-doctoral Supervisor: Prf. Mecatti), now accepted for publication.
Scientific objectives of visit
My main expectation would be to clearly show the advantages of a multivariate approach to poverty study. I believe that a non-standard recent statistical tool such as Chain Graphical Models would add value to the analysis both for its advanced methodology and its results as clear to understand and simple to be used even by non-technical users.
Reasons for choosing research infrastructure and datasets/surveys/...
TARKI offers the supervision expert in the statistical technique proposed by me and represents the main pillar on poverty and social exlusion of the InGRID.
Activities during your visit (research, training, events, ...)
The activities during my visit at TARKI entailed conducting research and analysis by using R software, on the TARKI Household Monitor survey, wave 2012, and meeting with the scientific
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responsable professor Tamas Rudas who gave me guidance and further useful instructions concerning the statistical method proposed
Method and set-up of research
Datasets collected from Household surveys (Monitor wave in 2012). Data was managed with
R statistical softwere.
Project achievements during visit (and possible difficulties encountered)
Deep study of the link between social and wehalt situation through chain graphical model.
Preliminary project results and conclusions
Preliminary projects results are about to come.
Outcomes and future studies
The outcome of my visit at TARKI will be peer-revied and potentially published in a scientific journal.
References
 Bergsma, W. P., Rudas, T. (2002) Marginal models for categorical data. Annals of
Statistics, 30, 140-159.
 Drton, M.: Discrete chain graph models. Bernoulli 15(3), 736–753 (2009)
 Nicolussi, F.: Marginal parameterizations for conditional independence models and graphical models for categorical data, PhD Thesis (2013)
 Nicolussi, F., Colombi, R.: Graphical model of type II: a smooth subclass. Working paper n. 224, http://boa .unimib.it/handle/10281/51950 (2014)
 Rudas, T., Bergsma, W., Nemeth, R. (2010) Marginal log-linear parameterization of conditional independence models.
Biometrika, 97(4), 1006-1012.
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