A stepwise procedure to select variables in a fuzzy least square regression model

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.