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A Bayesian Approach for Predicting with Polynomial Regresión of Unknown Degree

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  • Irwin Guttman
  • Daniel Peña

    ()

  • M Dolores Redondas

    ()

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    Abstract

    This article presents a comparison of four methods to compute the posterior probabilities of the possible orders in polynomial regression models. These posterior probabilities are used for forecasting by using Bayesian model averaging. It is shown that Bayesian model averaging provides a closer relationship between the theoretical coverage of the high density predictive interval (HDPI) and the observed coverage than those corresponding to selecting the best model. The performance of the different procedures are illustrated with simulations and some known engineering data.

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    File URL: http://docubib.uc3m.es/WORKINGPAPERS/WS/ws032104.pdf
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    Bibliographic Info

    Paper provided by Universidad Carlos III, Departamento de Estadística y Econometría in its series Statistics and Econometrics Working Papers with number ws032104.

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    Date of creation: Apr 2003
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    Handle: RePEc:cte:wsrepe:ws032104

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    1. Philips, R. & Guttman, I., 1998. "A new criterion for variable selection," Statistics & Probability Letters, Elsevier, vol. 38(1), pages 11-19, May.
    2. Carmen Fernández & Eduardo Ley & Mark F. J. Steel, 2002. "Bayesian modelling of catch in a north-west Atlantic fishery," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 51(3), pages 257-280.
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