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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Wang, Z | - |
dc.contributor.author | Gu, Q | - |
dc.contributor.author | Ning, Y | - |
dc.contributor.author | Liu, H | - |
dc.date.accessioned | 2021-10-11T14:16:46Z | - |
dc.date.available | 2021-10-11T14:16:46Z | - |
dc.date.issued | 2015 | en_US |
dc.identifier.citation | Wang, 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.issn | 1049-5258 | - |
dc.identifier.uri | http://papers.nips.cc/paper/5914-high-dimensional-em-algorithm-statistical-optimization-and-asymptotic-normality | - |
dc.identifier.uri | http://arks.princeton.edu/ark:/88435/pr17p3z | - |
dc.description.abstract | 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. | en_US |
dc.format.extent | 2521 - 2529 | en_US |
dc.language.iso | en_US | en_US |
dc.relation.ispartof | Advances in Neural Information Processing Systems | en_US |
dc.rights | Author's manuscript | en_US |
dc.title | High dimensional EM algorithm: Statistical optimization and asymptotic normality | en_US |
dc.type | Conference Article | en_US |
pu.type.symplectic | http://www.symplectic.co.uk/publications/atom-terms/1.0/conference-proceeding | en_US |
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File | Description | Size | Format | |
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HighDimEMAlgorOptimizNormal.pdf | 513.02 kB | Adobe PDF | View/Download |
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