Lasso-type sparse regression and high-dimensional Gaussian graphical models
High-dimensional datasets, where the number of measured variables is larger than the sample size, are not uncommon in modern real-world applications such as functional Magnetic Resonance Imaging (fMRI) data. Conventional statistical signal processing tools and mathematical models could fail at handl...
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| Language: | English |
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University of British Columbia
2012
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| Online Access: | http://hdl.handle.net/2429/42271 |