A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value situations, applications to grouped, censored or truncated data, finite mixture models, variance component estimation, hyperparameter estimation, iteratively reweighted least squares and factor analysis.
Series B (Statistical Methodology) of the Journal of the Royal Statistical Society started out simply as the Supplement to the Journal of the Royal Statistical Society in the Society's centenary year of 1934. The journal now publishes high quality papers on the methodological aspects of statistics. The objective of papers is to contribute to the understanding of statistical methodology and/or to develop and improve statistical methods. JSTOR provides a digital archive of the print version of Journal of the Royal Statistical Society, Series B: Statistical Methodology. The electronic version of Journal of the Royal Statistical Society, Series B: Statistical Methodology is available at http://www.blackwell-synergy.com/servlet/useragent?func=showIssues&code;=rssb. Authorized users may be able to access the full text articles at this site.
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Journal of the Royal Statistical Society. Series B (Methodological)
© 1977 Royal Statistical Society
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