Regression analysis using the imprecise Bayesian normal model

Lev V. Utkin

International Journal of Data Analysis Techniques and Strategies, 2010, vol. 2, issue 4, 356-372

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.

Keywords: imprecise probabilities; lower probability distributions; upper probability distributions; Vapnik; learning theory; risk; Bayesian inference; regression modelling; imprecise modelling; Bayesian normal model. (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:ids:injdan:v:2:y:2010:i:4:p:356-372

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