Regression analysis using the imprecise Bayesian normal model

A class of regression models taking into account the lack of sufficient statistical data is proposed. The main ideas of the class are to use the framework of Vapnik's learning theory and to replace a single probability distribution of the noise by a set of probability distributions, which is not the parametric set of distributions. The set of probability distributions is defined by the imprecise Bayesian normal model. A numerical example illustrates the proposed regression models.