A note on EM algorithm for mixture models

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dc.contributor.author Yao, Weixin
dc.date.accessioned 2013-01-22T17:24:19Z
dc.date.available 2013-01-22T17:24:19Z
dc.date.issued 2013-01-22
dc.identifier.uri http://hdl.handle.net/2097/15224
dc.description.abstract Expectation-maximization (EM) algorithm has been used to maximize the likelihood function or posterior when the model contains unobserved latent variables. One main important application of EM algorithm is to find the maximum likelihood estimator for mixture models. In this article, we propose an EM type algorithm to maximize a class of mixture type objective functions. In addition, we prove the monotone ascending property of the proposed algorithm and discuss some of its applications. en_US
dc.language.iso en_US en_US
dc.relation.uri http://www.sciencedirect.com/science/article/pii/S0167715212003896 en_US
dc.subject Adaptive regression en_US
dc.subject EM algorithm en_US
dc.subject Edge-preserving smoothers en_US
dc.subject Mode en_US
dc.subject Robust regression en_US
dc.title A note on EM algorithm for mixture models en_US
dc.type Article (author version) en_US
dc.date.published 2013 en_US
dc.citation.doi doi:10.1016/j.spl.2012.10.017 en_US
dc.citation.epage 526 en_US
dc.citation.issue 2 en_US
dc.citation.jtitle Statistics and Probability Letters en_US
dc.citation.spage 519 en_US
dc.citation.volume 83 en_US
dc.contributor.authoreid wxyao en_US

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