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A bayesian approach for predicting with polynomial regresión of unknown degree

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  • Guttman, Irwin
  • Peña, Daniel
  • Redondas, María Dolores

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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  • Guttman, Irwin & Peña, Daniel & Redondas, María Dolores, 2003. "A bayesian approach for predicting with polynomial regresión of unknown degree," DES - Working Papers. Statistics and Econometrics. WS ws032104, Universidad Carlos III de Madrid. Departamento de Estadística.
    • Handle: RePEc:cte:wsrepe:ws032104
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    References listed on IDEAS

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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.
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