Bayesian curve estimation by model averaging

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Show simple item record Peña, Daniel Redondas, María Dolores 2006-11-10T09:08:21Z 2006-11-10T09:08:21Z 2003-09
dc.description.abstract A bayesian approach is used to estimate a nonparametric regression model. The main features of the procedure are, first, the functional form of the curve is approximated by a mixture of local polynomials by Bayesian Model Averaging (BMA); second, the model weights are approximated by the BIC criterion, and third, a robust estimation procedure is incorporated to improve the smoothness of the estimated curve. The models considered at each sample points are polynomial regression models of order smaller that four, and the parameters of each model are estimated by a local window. The estimated value is computed by BMA, and the posterior probability of each model is approximated by the exponential of the BIC criterion. The robustness is achieved by assuming that the noise follows a scale contaminated normal model so that the effect of possible outliers is downweighted. The procedure provides a smooth curve and allows a straightforward prediction and quantification of the uncertainty. The method is illustrated with several examples and some Monte Carlo experiments.
dc.format.extent 667690 bytes
dc.format.mimetype application/pdf
dc.language.iso eng
dc.relation.ispartofseries UC3M Working Papers. Statistics and Econometrics
dc.relation.ispartofseries 2003-10
dc.title Bayesian curve estimation by model averaging
dc.type workingPaper
dc.subject.eciencia Estadística
dc.rights.accessRights openAccess
dc.identifier.repec ws034410
dc.affiliation.dpto UC3M. Departamento de Estadística
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