REGRESSION ANALYSIS AND FUZZY INFORMATION
In regression analysis usually it is assumed that observed data values are real numbers or vectors. In applications where continuous variables are observed this assumption is unrealistic. Therefore data have to be considered as fuzzy. Another kind of fuzzy information is present in Bayesian regression models. Here the a priori distributions of the parameters in regression models are typical examples of fuzzy information in the sense of fuzzy a priori distributions.
Both kinds of fuzzy information, fuzzy data as well as fuzzy probability distributions for the quantification of a priori knowledge concerning parameters has to be taken into account. This is the subject of this part.