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Regression analysis using the imprecise Bayesian normal model

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  • Lev V. Utkin
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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.

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    Bibliographic Info

    Article provided by Inderscience Enterprises Ltd in its journal Int. J. of Data Analysis Techniques and Strategies.

    Volume (Year): 2 (2010)
    Issue (Month): 4 ()
    Pages: 356-372

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    Handle: RePEc:ids:injdan:v:2:y:2010:i:4:p:356-372

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    Web page: http://www.inderscience.com/browse/index.php?journalID=282

    Related research

    Keywords: imprecise probabilities; lower probability distributions; upper probability distributions; Vapnik; learning theory; risk; Bayesian inference; regression modelling; imprecise modelling; Bayesian normal model.;

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