Three ways of implementing the EM algorithm when parameters are not identifiable
Biometrical Journal
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sg-nus-scholar.10635-1054322015-03-12T03:04:52Z Three ways of implementing the EM algorithm when parameters are not identifiable Yung Cheung Kuk, A. So Kuen Chan, J. STATISTICS & APPLIED PROBABILITY Identifiability constraints Incomplete data Monte Carlo methods Multivariate order statistics models Multivariate probit regression Biometrical Journal 43 2 207-218 2014-10-28T05:16:02Z 2014-10-28T05:16:02Z 2001 Article Yung Cheung Kuk, A.,So Kuen Chan, J. (2001). Three ways of implementing the EM algorithm when parameters are not identifiable. Biometrical Journal 43 (2) : 207-218. ScholarBank@NUS Repository. 03233847 http://scholarbank.nus.edu.sg/handle/10635/105432 NOT_IN_WOS Scopus |
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Identifiability constraints Incomplete data Monte Carlo methods Multivariate order statistics models Multivariate probit regression |
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Identifiability constraints Incomplete data Monte Carlo methods Multivariate order statistics models Multivariate probit regression Yung Cheung Kuk, A. So Kuen Chan, J. Three ways of implementing the EM algorithm when parameters are not identifiable |
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Biometrical Journal |
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STATISTICS & APPLIED PROBABILITY |
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STATISTICS & APPLIED PROBABILITY Yung Cheung Kuk, A. So Kuen Chan, J. |
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Article |
author |
Yung Cheung Kuk, A. So Kuen Chan, J. |
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Yung Cheung Kuk, A. |
title |
Three ways of implementing the EM algorithm when parameters are not identifiable |
title_short |
Three ways of implementing the EM algorithm when parameters are not identifiable |
title_full |
Three ways of implementing the EM algorithm when parameters are not identifiable |
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Three ways of implementing the EM algorithm when parameters are not identifiable |
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Three ways of implementing the EM algorithm when parameters are not identifiable |
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three ways of implementing the em algorithm when parameters are not identifiable |
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2014 |
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http://scholarbank.nus.edu.sg/handle/10635/105432 |
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