Title: "Joint estimation of multiple Gaussian graphical models by nonconvex penalty functions with an application to genomic data"
Speaker: Hyonho Chun, Department of Statistics, Purdue University
Place: HORT 117; March 22, 2011, Tuesday, 4:30pm


Inferring unknown gene regulation networks is one of key questions in systems bi- ology with important applications such as understanding disease physiology and drug discovery. These applications require inferring multiple networks in order to reveal the differences among different conditions. The multiple networks can be in- ferred by Gaussian graphical models by introducing sparsity on the inverse covari- ance matrices via penalization either individually or jointly. We propose a class of nonconvex penalty functions for the joint estimation of multiple Gaussian graphical models. Our approach is capable of regularizing both common and condition spe- cific associations without explicit parametrization as well as has oracle property for both common and specific associations. We show the performance of our nonconvex penalty functions by simulation study and then apply it to real genomic dataset.

Associated Reading:
Jian Guo, Elizaveta Levina, George Michailidis and Ji Zhu. 2011. Joint estimation of multiple graphical models. Biometrika (2011) 98 (1): 1-15.

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