Regression analysis using the imprecise Bayesian normal model

Authors:
Lev V. Utkin
Affiliations:
Department of Computer Science, St. Petersburg State Forest Technical Academy, Institutsky per. 5, St. Petersburg, 194021, Russia
Venue:
International Journal of Data Analysis Techniques and Strategies
Year:
2010

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Abstract

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.