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dc.contributor.authorLee, Jen_NZ
dc.contributor.authorVilla, C.en_NZ
dc.date.accessioned2016-01-22T00:01:19Z
dc.date.available2016-01-22T00:01:19Z
dc.date.copyright2016-01-01en_NZ
dc.identifier.citationRetrieved from http://arxiv.org/abs/1512.08077
dc.identifier.urihttp://hdl.handle.net/10292/9386
dc.description.abstractIn this work we discuss a novel model prior probability for variable selection in linear regression. The idea is to determine the prior mass in an objective sense, by considering the worth of each of the possible regression models, given the number of covariates under consideration. Through a simulation study, we show that the proposed prior outperforms the uniform prior and the Scott \& Berger prior in a scenario of no prior knowledge about the size of the true regression models. We illustrate the use of the prior using two well-known data sets with, respectively, 15 and 4 covariates.en_NZ
dc.publisherCornell University Library
dc.relation.urihttp://arxiv.org/abs/1512.08077en_NZ
dc.rightsarXiv.org Open access to 1,111,552 e-prints in Physics, Mathematics, Computer Science, Quantitative Biology, Quantitative Finance and Statistics.
dc.titleModel prior distribution for variable selection in linear regression modelsen_NZ
dc.typeJournal Article
dc.rights.accessrightsOpenAccessen_NZ
pubs.elements-id195994


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