Statistics Seminar

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Date/Time:Wednesday, 25 Jan 2012 from 4:10 pm to 5:00 pm
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
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"Sparse Matrix Graphical Models," Cheng Yong Tang, Department of Statistics & Applied Provbability, National University of Singapore

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.