Skip Navigation

Biometrika 1998 85(4):755-770; doi:10.1093/biomet/85.4.755
© 1998 by Biometrika Trust
This Article
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by LIU, C.
Right arrow Articles by WU, Y. N.
Right arrow Search for Related Content
Social Bookmarking
 Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

Parameter expansion to accelerate EM: The PX-EM algorithm

CHUANHAI LIU, DONALD B. RUBIN and YING NIAN WU

Bell Laboratories, Lucent Technologies Murray Hill, New Jersey 07974, U.S.A.liu{at}research.bell-labs.com
Department of Statistic, Harvard University Cambridge, Massachusetts 02138, U.S.A.rubin{at}hustat.harvard.edu
Department of Statistic, University of Michigan Ann Arbor, Michigan 48109, U.S.A.yingnian{at}umich.edu

The EM algorithm and its extensions are popular tools for modal estimation but are often criticised for their slow convergence. We propose a new method that can often make EM much faster. The intuitive idea is to use a ‘covariance adjustment’ to correct the analysis of the M step, capitalising on extra information captured in the imputed complete data. The way we accomplish this is by parameter expansion; we expand the complete-data model while preserving the observed-data model and use the expanded complete-data model to generate EM. This parameter-expanded EM, PX-EM, algorithm shares the simplicity and stability of ordinary EM, but has a faster rate of convergence since its M step performs a more efficient analysis. The PX-EM algorithm is illustrated for the multivariate t distribution, a random effects model, factor analysis, probit regression and a Poisson imaging model.

Key Words: AECM • Algorithms • Covariance adjustment • ECM • ECME • Factor analysis • Multivariate t distribution • Parameter expansion • Poisson imaging model • Probit regression • Random effects model


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
BiostatisticsHome page
J. C. Slaughter, A. H. Herring, and K. E. Hartmann
Bayesian modeling of embryonic growth using latent variables
Biostat., April 1, 2008; 9(2): 373 - 389.
[Abstract] [Full Text] [PDF]


Home page
Statistical ModellingHome page
J. Taylor and A. Verbyla
Joint modelling of location and scale parameters of the t distribution
Statistical Modeling, July 1, 2004; 4(2): 91 - 112.
[Abstract] [PDF]


Home page
JOURNAL OF EDUCATIONAL AND BEHAVIORAL STATISTICSHome page
D. B. Rubin
Teaching Statistical Inference for Causal Effects in Experiments and Observational Studies
Journal of Educational and Behavioral Statistics, January 1, 2004; 29(3): 343 - 367.
[Abstract] [PDF]



Disclaimer:
Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.