Statistics Seminar
Date/Time: | Wednesday, 25 Jan 2012 from 4:10 pm to 5:00 pm |
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Location: | Snedecor 3105 |
Cost: | Free |
URL: | www.stat.iastate.edu |
Contact: | Jeanette La Grange |
Phone: | 515-294-3440 |
Channel: | College of Liberal Arts and Sciences |
Categories: | Academic calendar Lectures |
Actions: | Download iCal/vCal | Email Reminder |
Matrix-variate observations are frequently encountered in many contemporary statistical problems due to a rising need to organize and analyze data with structural information. In this paper, we propose a novel sparse matrix graphical model for this type of statistical problems. By penalizing respectively two precision matrices corresponding to the rows and columns, our method yields a sparse matrix graphical model that synthetically characterizes the underlying conditional independence structure. Our model is more parsimonious and is practically more interpretable than the conventional sparse vector-variate graphical models. Asymptotic analysis shows that our penalized likelihood estimates enjoy better convergent rate than that of the vector-variate graphical model. The finite sample performance of the proposed method is illustrated via extensive simulation studies and several real datasets analysis. This is a joint work with Chenlei Leng.