Bayesian Hierarchical Model for Large-Scale Covariance Matrix Estimation
dc.contributor.author | Zhu, Dongxiao | en_US |
dc.contributor.author | Hero III, Alfred O. | en_US |
dc.date.accessioned | 2009-07-10T19:08:13Z | |
dc.date.available | 2009-07-10T19:08:13Z | |
dc.date.issued | 2007-12-01 | en_US |
dc.identifier.citation | Zhu, Dongxiao; Hero III, Alfred O. (2007). "Bayesian Hierarchical Model for Large-Scale Covariance Matrix Estimation." Journal of Computational Biology 14(10): 1311-1326 <http://hdl.handle.net/2027.42/63303> | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/63303 | |
dc.identifier.uri | http://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=18052776&dopt=citation | en_US |
dc.description.abstract | Many bioinformatics problems implicitly depend on estimating large-scale covariance matrix. The traditional approaches tend to give rise to high variance and low accuracy due to “overfitting.” We cast the large-scale covariance matrix estimation problem into the Bayesian hierarchical model framework, and introduce dependency between covariance parameters. We demonstrate the advantages of our approaches over the traditional approaches using simulations and OMICS data analysis. | en_US |
dc.format.extent | 938873 bytes | |
dc.format.extent | 2489 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.publisher | Mary Ann Liebert, Inc., publishers | en_US |
dc.title | Bayesian Hierarchical Model for Large-Scale Covariance Matrix Estimation | en_US |
dc.type | Article | en_US |
dc.subject.hlbtoplevel | Health Sciences | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.identifier.pmid | 18052776 | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/63303/1/cmb.2006.0151.pdf | |
dc.identifier.doi | doi:10.1089/cmb.2006.0151 | en_US |
dc.identifier.source | Journal of Computational Biology | en_US |
dc.identifier.source | Journal of Computational Biology | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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