Publication:
Bayesian curve estimation by model averaging

dc.affiliation.dptoUC3M. Departamento de Estadísticaes
dc.contributor.authorPeña, Daniel
dc.contributor.authorRedondas, María Dolores
dc.date.accessioned2006-11-10T09:08:21Z
dc.date.available2006-11-10T09:08:21Z
dc.date.issued2003-09
dc.description.abstractA 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.extent667690 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.repecws034410
dc.identifier.urihttps://hdl.handle.net/10016/422
dc.language.isoeng
dc.relation.ispartofseriesUC3M Working Papers. Statistics and Econometrics
dc.relation.ispartofseries2003-10
dc.rights.accessRightsopen access
dc.subject.ecienciaEstadística
dc.titleBayesian curve estimation by model averaging
dc.typeworking paper*
dspace.entity.typePublication
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