Skip to main content
Skip to main menu


Hui Zou

<a href="">University of Minnesota</a>

The Ising model is a useful tool for studying complex interactions within a system. The estimation of such a model, however, is rather challenging especially in the presence of high dimensional parameters. In this work, we propose efficient procedures for learning a sparse Ising model based on a penalized composite likelihood with non-concave penalties. Non-concave penalized likelihood estimation has received a lot of attention in recent years. However, such an approach is computationally prohibitive under high dimensional Ising models. To overcome such difficulties, we extend the methodology and theory of non-concave penalized likelihood to penalized composite likelihood estimation. An efficient solution path algorithm is devised by using a new coordinate-minorization-ascent algorithm. Asymptotic oracle properties of the proposed estimator are established with NP-dimensionality. We demonstrate its finite sample performance via simulation studies and further illustrate our proposal by studying the Human Immunodeficiency Virus type 1 (HIV-1) protease structure based on data from the Stanford HIV Drug Resistance Database. This talk is based on a joint paper with Lingzhou Xue and Tianxi Cai.

Support us

We appreciate your financial support. Your gift is important to us and helps support critical opportunities for students and faculty alike, including lectures, travel support, and any number of educational events that augment the classroom experience. Click here to learn more about giving.

Every dollar givenĀ has a direct impact upon ourĀ students and faculty.