EconStor >
Humboldt-Universität zu Berlin >
CASE - Center for Applied Statistics and Economics, Humboldt-Universität Berlin >
Papers, CASE - Center for Applied Statistics and Economics, HU Berlin >

Please use this identifier to cite or link to this item:

Full metadata record

DC FieldValueLanguage
dc.contributor.authorMcLachlan, Geoffrey J.en_US
dc.contributor.authorKrishnan, Thriyambakamen_US
dc.contributor.authorNg, See Keten_US
dc.description.abstractThe 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.en_US
dc.relation.ispartofseriesPapers / Humboldt-Universität Berlin, Center for Applied Statistics and Economics (CASE) 2004,24en_US
dc.titleThe EM Algorithmen_US
dc.typeWorking Paperen_US
Appears in Collections:Papers, CASE - Center for Applied Statistics and Economics, HU Berlin

Files in This Item:
File Description SizeFormat
24_tk_gm_skn.pdf275.46 kBAdobe PDF
No. of Downloads: Counter Stats
Show simple item record
Download bibliographical data as: BibTeX

Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.