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High dimensional EM algorithm: Statistical optimization and asymptotic normality

Author(s): Wang, Z; Gu, Q; Ning, Y; Liu, H

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dc.contributor.authorWang, Z-
dc.contributor.authorGu, Q-
dc.contributor.authorNing, Y-
dc.contributor.authorLiu, H-
dc.date.accessioned2021-10-11T14:16:46Z-
dc.date.available2021-10-11T14:16:46Z-
dc.date.issued2015en_US
dc.identifier.citationWang, Zhaoran, Quanquan Gu, Yang Ning, and Han Liu. "High dimensional em algorithm: Statistical optimization and asymptotic normality." In Advances in neural information processing systems, (2015): pp. 2521-2529.en_US
dc.identifier.issn1049-5258-
dc.identifier.urihttp://papers.nips.cc/paper/5914-high-dimensional-em-algorithm-statistical-optimization-and-asymptotic-normality-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr17p3z-
dc.description.abstractWe 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.en_US
dc.format.extent2521 - 2529en_US
dc.language.isoen_USen_US
dc.relation.ispartofAdvances in Neural Information Processing Systemsen_US
dc.rightsAuthor's manuscripten_US
dc.titleHigh dimensional EM algorithm: Statistical optimization and asymptotic normalityen_US
dc.typeConference Articleen_US
pu.type.symplectichttp://www.symplectic.co.uk/publications/atom-terms/1.0/conference-proceedingen_US

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