An interval-based approach to fuzzy regression for fuzzy input-output data

A novel approach is introduced to construct a fuzzy regression model when the data available of independent and dependent variables are fuzzy numbers. The approach, consisting on the least-squares method, uses the α-level sets of fuzzy observations to estimate the crisp parameters of the model. A competitive study shows the performance and efficiency of the proposed approach with respect to some well-known methods.

I. Introduction

Identification and analysis of the functional relationship between a dependent and some independent variables have made great interest in statistical analysis. Based on such functional relationship, which is called regression model, one can describe and predict the values of the dependent variable using the observations of independent variables.