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Poster
High Dimensional EM Algorithm: Statistical Optimization and Asymptotic Normality
Zhaoran Wang · Quanquan Gu · Yang Ning · Han Liu

Wed Dec 09 04:00 PM -- 08:59 PM (PST) @ 210 C #73
in Poster Session »

We provide a general theory of the expectation-maximization (EM) algorithm for inferring high dimensional latent variable models. In particular, we make two contributions: (i) For parameter estimation, we propose a novel high dimensional EM algorithm which naturally incorporates sparsity structure into parameter estimation. With an appropriate initialization, this algorithm converges at a geometric rate and attains an estimator with the (near-)optimal statistical rate of convergence. (ii) Based on the obtained estimator, we propose a new inferential procedure for testing hypotheses for low dimensional components of high dimensional parameters. For a broad family of statistical models, our framework establishes the first computationally feasible approach for optimal estimation and asymptotic inference in high dimensions.

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Author Information

Zhaoran Wang (Princeton University)
Quanquan Gu (University of Virginia)
Yang Ning (Princeton University)
Han Liu (Princeton University)

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