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dc.contributor.author Li, F
dc.contributor.author Zhang, NR
dc.date.accessioned 2011-06-21T17:30:31Z
dc.date.issued 2010-09-01
dc.identifier.citation Journal of the American Statistical Association, 2010, 105 (491), pp. 1202 - 1214
dc.identifier.issn 0162-1459
dc.identifier.uri http://hdl.handle.net/10161/4400
dc.description.abstract We consider the problem of variable selection in regression modeling in high-dimensional spaces where there is known structure among the covariates. This is an unconventional variable selection problem for two reasons: (1) The dimension of the covariate space is comparable, and often much larger, than the number of subjects in the study, and (2) the covariate space is highly structured, and in some cases it is desirable to incorporate this structural information in to the model building process. We approach this problem through the Bayesian variable selection framework, where we assume that the covariates lie on an undirected graph and formulate an Ising prior on the model space for incorporating structural information. Certain computational and statistical problems arise that are unique to such high-dimensional, structured settings, the most interesting being the phenomenon of phase transitions. We propose theoretical and computational schemes to mitigate these problems. We illustrate our methods on two different graph structures: the linear chain and the regular graph of degree k. Finally, we use our methods to study a specific application in genomics: the modeling of transcription factor binding sites in DNA sequences. © 2010 American Statistical Association.
dc.format.extent 1202 - 1214
dc.language.iso en_US en_US
dc.relation.ispartof Journal of the American Statistical Association
dc.relation.isversionof 10.1198/jasa.2010.tm08177
dc.title Bayesian variable selection in structured high-dimensional covariate spaces with applications in genomics
dc.title.alternative en_US
dc.type Journal Article
dc.description.version Version of Record en_US
duke.date.pubdate 2010-9-0 en_US
duke.description.endpage 1214 en_US
duke.description.issue 491 en_US
duke.description.startpage 1202 en_US
duke.description.volume 105 en_US
dc.relation.journal Journal of the American Statistical Association en_US
pubs.issue 491
pubs.organisational-group /Duke
pubs.organisational-group /Duke/Trinity College of Arts & Sciences
pubs.organisational-group /Duke/Trinity College of Arts & Sciences/Statistical Science
pubs.publication-status Published
pubs.volume 105

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