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http://hdl.handle.net/11375/11779
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DC Field | Value | Language |
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dc.contributor.advisor | Beyene, Joseph | en_US |
dc.contributor.advisor | Narayanaswamy Balakrishnan and Aaron Childs | en_US |
dc.contributor.author | Pichika, Sathish chandra | en_US |
dc.date.accessioned | 2014-06-18T16:56:49Z | - |
dc.date.available | 2014-06-18T16:56:49Z | - |
dc.date.created | 2011-12-24 | en_US |
dc.date.issued | 2012-04 | en_US |
dc.identifier.other | opendissertations/6721 | en_US |
dc.identifier.other | 7714 | en_US |
dc.identifier.other | 2424107 | en_US |
dc.identifier.uri | http://hdl.handle.net/11375/11779 | - |
dc.description.abstract | <p>Canonical Correlation Analysis (CCA) is one of the multivariate statistical methods that can be used to find relationship between two sets of variables. I highlighted challenges in analyzing high-dimensional data with CCA. Recently, Sparse CCA (SCCA) methods have been proposed to identify sparse linear combinations of two sets of variables with maximal correlation in the context of high-dimensional data. In my thesis, I compared three different SCCA approaches. I evaluated the three approaches as well as the classical CCA on simulated datasets and illustrated the methods with publicly available genomic and proteomic datasets.</p> | en_US |
dc.subject | CCA | en_US |
dc.subject | SCCA | en_US |
dc.subject | High-Dimensional | en_US |
dc.subject | Multivariate Analysis | en_US |
dc.subject | Multivariate Analysis | en_US |
dc.title | Sparse Canonical Correlation Analysis (SCCA): A Comparative Study | en_US |
dc.type | thesis | en_US |
dc.contributor.department | Mathematics and Statistics | en_US |
dc.description.degree | Master of Science (MSc) | en_US |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Size | Format | |
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fulltext.pdf | 448.26 kB | Adobe PDF | View/Open |
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