Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/22198
Authors: 
McLachlan, Geoffrey J.
Krishnan, Thriyambakam
Ng, See Ket
Year of Publication: 
2004
Series/Report no.: 
Papers / Humboldt-Universität Berlin, Center for Applied Statistics and Economics (CASE) 2004,24
Abstract: 
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.
Document Type: 
Working Paper

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