A STEPWISE PROCEDURE TO SELECT VARIABLES IN A FUZZY LEAST SQUARE REGRESSION MODEL

Francesco Campobasso, Annarita Fanizzi

2011

Abstract

Fuzzy regression techniques can be used to fit fuzzy data into a regression model. Diamond treated the case of a simple model introducing a metrics into the space of triangular fuzzy numbers. In previous works we provided some theoretical results about the estimates of a multiple regression model with a non-fuzzy intercept; in this paper we show how the sum of squares of the dependent variable can be decomposed in exactly the same way as the classical OLS estimation procedure only when the intercept is fuzzy asymmetric. Such a decomposition allows us to introduce a stepwise procedure which simplifies, in terms of computational, the identification of the most significant independent variables in the model.

Paper Citation

in Harvard Style

Campobasso F. and Fanizzi A. (2011). A STEPWISE PROCEDURE TO SELECT VARIABLES IN A FUZZY LEAST SQUARE REGRESSION MODEL . In Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: FCTA, (IJCCI 2011) ISBN 978-989-8425-83-6, pages 417-426. DOI: 10.5220/0003720504170426

in Bibtex Style

@conference{fcta11,
author={Francesco Campobasso and Annarita Fanizzi},
title={A STEPWISE PROCEDURE TO SELECT VARIABLES IN A FUZZY LEAST SQUARE REGRESSION MODEL},
booktitle={Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: FCTA, (IJCCI 2011)},
year={2011},
pages={417-426},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003720504170426},
isbn={978-989-8425-83-6},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: FCTA, (IJCCI 2011)
TI - A STEPWISE PROCEDURE TO SELECT VARIABLES IN A FUZZY LEAST SQUARE REGRESSION MODEL
SN - 978-989-8425-83-6
AU - Campobasso F.
AU - Fanizzi A.
PY - 2011
SP - 417
EP - 426
DO - 10.5220/0003720504170426