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Issued date: 2003-04

Serie-No.: UC3M Working Papers. Statistics and Econometrics2003-04

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 BayesiaThis 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.+-





Author: Guttman, Irwin; Peña, Daniel; Redondas, María Dolores

Source: http://e-archivo.uc3m.es


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Universidad Carlos III de Madrid Repositorio institucional e-Archivo http:--e-archivo.uc3m.es Departamento de Estadística DES - Working Papers.
Statistics and Econometrics.
WS 2003-04 A bayesian approach for predicting with polynomial regresión of unknown degree. Guttman, Irwin http:--hdl.handle.net-10016-192 Descargado de e-Archivo, repositorio institucional de la Universidad Carlos III de Madrid Working Paper 03-21 Statistics and Econometrics Series 04 April 2003 Departamento de Estadística y Econometría Universidad Carlos III de Madrid Calle Madrid, 126 28903 Getafe (Spain) Fax (34) 91 624-98-49 A Bayesian Approach for Predicting with Polynomial Regresión of Unknown Degree. Irwin Guttman, Daniel Peña and M Dolores Redondas* 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. Key words: Bayesian Model Averaging; Fractional Bayes Factor; Intrinsic Bayes Factor; Bayesian Information Criterion. *Guttman, Statistics Madrid, State University and Econometrics Spain, e-mail: of New York Department, dpena@est-econ.uc3m.es, , Buffalo. University Redondas, U.S.A. Carlos Peña, III Statistics of and Econometrics Department, University Carlos III of Madrid, Spain, email: redondas@est-econ.uc3m.es.Tel: 34 916249314. financial support from BEC2000-0167, MCYT, Spain. We also acknowledge A Bayesian Approach for Predicting with Polynomial Regression of Unknown Degree Irwin Guttman, Daniel Peña and Dolores Re...





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