04d79f31b4a6d310VgnVCM100000c2b1d38dRCRDapproved/UMICH/stats/Home/News & Events/Archived Events/2010-2011 EventsAshin Mukherjee###@###(Thu, 9 Dec 2010)Ashin Mukherjee###@###(Thu, 9 Dec 2010)438 West HallReduced Rank Ridge Regression and Its Kernel Extensionsstats129192300000012919230000002:30 PM<p><b>Title:</b> Reduced Rank Ridge Regression and Its Kernel Extensions<br> <b>Advisor: </b>Associate Professor Ji Zhu<br> <b>Committee Members: </b>Professor Naisyin Wang, Associate Professor Kerby Shedden</p> <p><b>Abstract:</b> In multivariate linear regression, it is often assumed that the response matrix is intrinsically of lower rank. This could be due to the correlation structure among the predictor variables or the coefficient matrix being lower rank. To accommodate both, we propose a reduced rank ridge regression for multivariate linear regression. Specifically, we combine the ridge penalty with the reduced rank constraint on the coefficient matrix to come up with a computationally straightforward algorithm. Numerical studies indicate that the proposed method consistently outperforms relevant competitors. A novel extension of the proposed method to the reproducing kernel Hilbert space (RKHS) set-up is also developed.</p>Njjsantosjjsantos1366830943217d3d79f31b4a6d310VgnVCM100000c2b1d38d____once11112newnewEvent Flyer/UMICH/stats/Home/Events/Dissertations and Oral Preliminary Examinations/Ashin Mukherjee Defense Flyer.pdfAshin Mukherjee