Effect on Prediction When Modeling Covariates in Bayesian Nonparametric Models

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dc.citation.firstpage204en_US
dc.citation.journalTitleJournal of Statistical Theory and Practiceen_US
dc.citation.lastpage218en_US
dc.citation.volumeNumber7en_US
dc.contributor.authorCruz-Marcelo, Alejandroen_US
dc.contributor.authorRosner, Gary L.en_US
dc.contributor.authorMüller, Peteren_US
dc.contributor.authorStewart, Clinton F.en_US
dc.contributor.orgCenter for Computational Finance and Economic Systemsen_US
dc.date.accessioned2022-05-25T16:13:36Zen_US
dc.date.available2022-05-25T16:13:36Zen_US
dc.date.issued2013en_US
dc.description.abstractIn biomedical research, it is often of interest to characterize biologic processes giving rise to observations and to make predictions of future observations. Bayesian nonparamric methods provide a means for carrying out Bayesian inference making as few assumptions about restrictive parametric models as possible. There are several proposals in the literature for extending Bayesian nonparametric models to include dependence on covariates. In this article, we examine the effect on fitting and predictive performance of incorporating covariates in a class of Bayesian nonparametric models by one of two primary ways: either in the weights or in the locations of a discrete random probability measure. We show that different strategies for incorporating continuous covariates in Bayesian nonparametric models can result in big differences when used for prediction, even though they lead to otherwise similar posterior inferences. When one needs the predictive density, as in optimal design, and this density is a mixture, it is better to make the weights depend on the covariates. We demonstrate these points via a simulated data example and in an application in which one wants to determine the optimal dose of an anticancer drug used in pediatric oncology.en_US
dc.identifier.citationCruz-Marcelo, Alejandro, Rosner, Gary L., Müller, Peter, et al.. "Effect on Prediction When Modeling Covariates in Bayesian Nonparametric Models." <i>Journal of Statistical Theory and Practice,</i> 7, (2013) Springer Nature: 204-218. https://doi.org/10.1080/15598608.2013.772811.en_US
dc.identifier.doihttps://doi.org/10.1080/15598608.2013.772811en_US
dc.identifier.urihttps://hdl.handle.net/1911/112407en_US
dc.language.isoengen_US
dc.publisherSpringer Natureen_US
dc.rightsThis is an author's pre-print. The published article is copyrighted by Springer Nature.en_US
dc.titleEffect on Prediction When Modeling Covariates in Bayesian Nonparametric Modelsen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpost-printen_US
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