SOME RESULTS ON A MULTIVARIATE GENERALIZATION OF THE FUZZY LEAST SQUARE REGRESSION

Francesco Campobasso, Annarita Fanizzi, Marina Tarantini

Abstract

Fuzzy regression techniques can be used to fit fuzzy data into a regression model, where the deviations between the dependent variable and the model are connected with the uncertain nature either of the variables or of their coefficients. P.M. Diamond (1988) treated the case of a simple fuzzy regression of an uncertain dependent variable on a single uncertain independent variable, introducing a metrics into the space of triangular fuzzy numbers. In this work we managed more than a single independent variable, determining the corresponding estimates and providing some theoretical results about the decomposition of the sum of squares of the dependent variable according to Diamond’s metric, in order to identify its components.

References

  1. Campobasso, F., Fanizzi, A., Tarantini, M., 2008, Fuzzy Least Square Regression, Annals of Department of Statistical Sciences, University of Bari, Italy, 229-243.
  2. Diamond, P. M., 1988. Fuzzy Least Square, Information Sciences, 46:141-157.
  3. Kao, C.,Chyu, C.L., 2003, Least-squares estimates in fuzzy regression analysis, European Journal of Operational Research, 148:426-435.
  4. Takemura, K., 2005. Fuzzy least squares regression analysis for social judgment study, Journal of Advanced Intelligent Computing and Intelligent Informatics, 9(5), 461:466.
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Paper Citation


in Harvard Style

Campobasso F., Fanizzi A. and Tarantini M. (2009). SOME RESULTS ON A MULTIVARIATE GENERALIZATION OF THE FUZZY LEAST SQUARE REGRESSION . In Proceedings of the International Joint Conference on Computational Intelligence - Volume 1: ICFC, (IJCCI 2009) ISBN 978-989-674-014-6, pages 75-78. DOI: 10.5220/0002321200750078


in Bibtex Style

@conference{icfc09,
author={Francesco Campobasso and Annarita Fanizzi and Marina Tarantini},
title={SOME RESULTS ON A MULTIVARIATE GENERALIZATION OF THE FUZZY LEAST SQUARE REGRESSION},
booktitle={Proceedings of the International Joint Conference on Computational Intelligence - Volume 1: ICFC, (IJCCI 2009)},
year={2009},
pages={75-78},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002321200750078},
isbn={978-989-674-014-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Joint Conference on Computational Intelligence - Volume 1: ICFC, (IJCCI 2009)
TI - SOME RESULTS ON A MULTIVARIATE GENERALIZATION OF THE FUZZY LEAST SQUARE REGRESSION
SN - 978-989-674-014-6
AU - Campobasso F.
AU - Fanizzi A.
AU - Tarantini M.
PY - 2009
SP - 75
EP - 78
DO - 10.5220/0002321200750078