The EM Algorithm
Geoffrey J. McLachlan,
Thriyambakam Krishnan and
See Ket Ng
No 2004,24, Papers from Humboldt University of Berlin, Center for Applied Statistics and Economics (CASE)
The Expectation-Maximization (EM) algorithm is a broadly applicable approach to the iterative computation of maximum likelihood (ML) estimates, useful in a variety of incomplete-data problems. Maximum likelihood estimation and likelihood-based inference are of central importance in statistical theory and data analysis. Maximum likelihood estimation is a general-purpose method with attractive properties. It is the most-often used estimation technique in the frequentist framework; it is also relevant in the Bayesian framework (Chapter III.11). Often Bayesian solutions are justified with the help of likelihoods and maximum likelihood estimates (MLE), and Bayesian solutions are similar to penalized likelihood estimates. Maximum likelihood estimation is an ubiquitous technique and is used extensively in every area where statistical techniques are used.
References: View references in EconPapers View complete reference list from CitEc
Citations View citations in EconPapers (7) Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:zbw:caseps:200424
Access Statistics for this paper
More papers in Papers from Humboldt University of Berlin, Center for Applied Statistics and Economics (CASE) Contact information at EDIRC.
Bibliographic data for series maintained by ZBW - Leibniz Information Centre for Economics ().