A Bayesian approach to hybrid splines non-parametric regression

dc.creatorDias, R
dc.creatorGamerman, D
dc.date2002
dc.dateAPR
dc.date2014-11-16T14:24:12Z
dc.date2015-11-26T16:23:21Z
dc.date2014-11-16T14:24:12Z
dc.date2015-11-26T16:23:21Z
dc.date.accessioned2018-03-28T23:04:36Z
dc.date.available2018-03-28T23:04:36Z
dc.identifierJournal Of Statistical Computation And Simulation. Taylor & Francis Ltd, v. 72, n. 4, n. 285, n. 297, 2002.
dc.identifier0094-9655
dc.identifierWOS:000177294100003
dc.identifier10.1080/00949650290007342
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/52727
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/52727
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/52727
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1268245
dc.descriptionA Bayesian approach is considered to estimate the number of basis functions and the smoothing parameter of the hybrid splines non-parametric regression procedure. The method used to obtain the estimate of the regression curve and its Bayesian confidence intervals is based on the reversible jump MCMC (Green 1995). Illustrations with simulated data are provided and show good performance of the proposed approach over the existing methods.
dc.description72
dc.description4
dc.description285
dc.description297
dc.languageen
dc.publisherTaylor & Francis Ltd
dc.publisherAbingdon
dc.publisherInglaterra
dc.relationJournal Of Statistical Computation And Simulation
dc.relationJ. Stat. Comput. Simul.
dc.rightsfechado
dc.rightshttp://journalauthors.tandf.co.uk/permissions/reusingOwnWork.asp
dc.sourceWeb of Science
dc.subjecthybrid splines non-parametric regression
dc.subjectreversible jump MCMC
dc.subjectNonparametric Regression
dc.subjectConfidence-intervals
dc.titleA Bayesian approach to hybrid splines non-parametric regression
dc.typeArtículos de revistas


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