A bayesian approach for predicting with polynomial regresión of unknown degree
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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- Philips, R. & Guttman, I., 1998. "A new criterion for variable selection," Statistics & Probability Letters, Elsevier, vol. 38(1), pages 11-19, May.
- Carmen Fernandez & Eduardo Ley & Mark Steel, 2001.
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- 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.
- Carmen Fernandez & Eduardo Ley & Mark F J Steel, 2001. "Bayesian modelling of catch in a Northwest Atlantic Fishery," ESE Discussion Papers 67, Edinburgh School of Economics, University of Edinburgh.
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