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An Analysis of Poverty in Italy through a Fuzzy Regression Model

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Computational Science and Its Applications - ICCSA 2011 (ICCSA 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6782))

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Abstract

Over recent years, and related in particular to the significant recent international economic crisis, an increasingly worrying rise in poverty levels has been observed both in Italy, as well as in other countries. Such a phenomenon may be analysed from an objective perspective (i.e. in relation to the macro and micro-economic causes by which it is determined) or, rather, from a subjective perspective (i.e. taking into consideration the point of view of individuals or families who locate themselves as being in a condition of hardship). Indeed, the individual “perception” of a state of being allows for the identification of measures of poverty levels to a much greater degree than would the assessment of an external observer. For this reason, experts in the field have, in recent years, attempted to overcome the limitations of traditional approaches, focusing instead on a multidimensional approach towards social and economic hardship, equipping themselves with a wide range of indicators on living conditions, whilst simultaneously adopting mathematical tools which allow for a satisfactory investigation of the complexity of the phenomenon under examination. The present work elaborates on data revealed by the EU-SILC survey of 2006 regarding the perception of poverty by Italian families, through a fuzzy regression model, with the aim of identifying the most relevant factors over others in influencing such perceptions.

The contribution is the result of joint reflections by the authors, with the following contributions attributed to S. Montrone (chapter 4), to F. Campobasso (chapter 1 and 2), to P. Perchinunno (chapter 3.1 and 3.2), and to A. Fanizzi (chapter 3.3).

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References

  1. Cammarata, S.: Sistemi a logica fuzzy. Come rendere intelligenti le macchine, ETAS (1997)

    Google Scholar 

  2. Campobasso, F., Fanizzi, A., Tarantini, M.: Una generalizzazione multivariata della Fuzzy Least Square Regression. Annali del Dipartimento di Scienze Statistiche dell’Università degli Studi di Bari 7, 229–243 (2008)

    Google Scholar 

  3. Campobasso, F., Fanizzi, A., Tarantini, M.: Some results on a multivariate generalization of the Fuzzy Least Square Regression. In: Proceedings of the International Conference on Fuzzy Computation, Madeira, pp. 75–78 (2009)

    Google Scholar 

  4. Diamond, p.M.: Fuzzy Least Square. Information Sciences 46, 141–157 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  5. ISTAT (2006), EU-SILC, the European Standard on Income and Living Conditions, Anno (2006)

    Google Scholar 

  6. Kao, C., Chyu, C.L.: Least-squares estimates in fuzzy regression analysis. European Journal of Operational Research 148, 426–435 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  7. Kosko, Bart.: Fuzzy Thinking: The New Science of Fuzzy Logic. Hyperion (1993) ISBN 0-7868-8021-X

    Google Scholar 

  8. Lenoir, R.: Les Exclus. Un francais surd ix. Seuil, Paris (1974)

    Google Scholar 

  9. Montrone, S., et al.: A Fuzzy Approach to the Small Area Estimation of Poverty in Italy. In: Phillips-Wren, G., et al. (eds.) Advances in Intelligent Decision Technologies, Smart Innovation, Systems and Technologies, vol. 4, pp. 309–318. Springer, Heidelberg (2010), ISSN 2190-3018, ISBN 978-3-642-14615-2, DOI 10.1007/978-3-642-14616-9

    Chapter  Google Scholar 

  10. Sen, A.: Well-Being, Capability and Public Policy, Giornale degli Economisti e Analisi di Economia, vol. 3 (1994)

    Google Scholar 

  11. Tanaka, H., Uejima, S., Asai, K.: Regression analysis with fuzzy model. In: IEEE Transactions on Systems, Man, and Cybernetics SMC, vol. 12, pp. 903–907 (1982)

    Google Scholar 

  12. Takemura, K.: Fuzzy least squares regression analysis for social judgment study. Journal of Advanced Intelligent Computing and Intelligent Informatics 9(5), 461–466 (2005)

    Article  Google Scholar 

  13. Veronesi, M., Visioli, A.: Logica fuzzy. Fondamenti teorici e applicazioni pratiche. Franco Angeli, Milano (2003)

    Google Scholar 

  14. Zadeh, L.A.: Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems 1(1), 3–28 (1978)

    Article  MathSciNet  MATH  Google Scholar 

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Montrone, S., Campobasso, F., Perchinunno, P., Fanizzi, A. (2011). An Analysis of Poverty in Italy through a Fuzzy Regression Model. In: Murgante, B., Gervasi, O., Iglesias, A., Taniar, D., Apduhan, B.O. (eds) Computational Science and Its Applications - ICCSA 2011. ICCSA 2011. Lecture Notes in Computer Science, vol 6782. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21928-3_24

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  • DOI: https://doi.org/10.1007/978-3-642-21928-3_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21927-6

  • Online ISBN: 978-3-642-21928-3

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