Regression analysis using the imprecise Bayesian normal model. (15th December 2010)

• Record Type: Journal Article
• Authors: Utkin, Lev V.
• Is Part Of: International journal of data analysis techniques and strategies. Volume 2:Number 4(2010)
• Page Start: 356
• Page End: 372
• Publication Date: 2010-12-15
• Subjects: imprecise probabilities -- lower probability distributions -- upper probability distributions -- Vapnik -- learning theory -- risk -- Bayesian inference -- regression modelling -- imprecise modelling -- Bayesian normal model; Electronic data processing -- Periodicals; Database searching -- Periodicals; 005
• Languages: English
• ISSNs: 1755-8050

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.