Effect on Prediction When Modeling Covariates in Bayesian Nonparametric Models

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dc.citation.firstpage204
dc.citation.journalTitleJournal of Statistical Theory and Practice
dc.citation.lastpage218
dc.citation.volumeNumber7
dc.contributor.authorCruz-Marcelo, Alejandro
dc.contributor.authorRosner, Gary L.
dc.contributor.authorMüller, Peter
dc.contributor.authorStewart, Clinton F.
dc.contributor.orgCenter for Computational Finance and Economic Systems
dc.date.accessioned2022-05-25T16:13:36Z
dc.date.available2022-05-25T16:13:36Z
dc.date.issued2013
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.
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.
dc.identifier.doihttps://doi.org/10.1080/15598608.2013.772811
dc.identifier.urihttps://hdl.handle.net/1911/112407
dc.language.isoeng
dc.publisherSpringer Nature
dc.rightsThis is an author's pre-print. The published article is copyrighted by Springer Nature.
dc.titleEffect on Prediction When Modeling Covariates in Bayesian Nonparametric Models
dc.typeJournal article
dc.type.dcmiText
dc.type.publicationpost-print
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