Expectation-maximization estimators for incompletely observed data | Zendy

Vladimir Vasić | Zendy

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Expectation-maximization estimators for incompletely observed data

Author(s) -

Vladimir Vasić

Publication year - 2004

Publication title -

economic annals

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.148

H-Index - 12

eISSN - 1820-7375

pISSN - 0013-3264

DOI - 10.2298/eka0461165v

Subject(s) - expectation–maximization algorithm , maximization , estimator , maximum likelihood , variety (cybernetics) , computation , mathematical optimization , computer science , maximum likelihood sequence estimation , restricted maximum likelihood , mathematics , statistics , algorithm

Expectation-maximization is a broadly applicable approach to the iterative computation of maximum likelihood estimates. Each iteration of expectation-maximization method consists of two steps: the expectation step and the maximization step. Expectation-maximization method is useful in a variety of problems where the maximum likelihood estimates are very difficult to find. The basic idea of expectation-maximization method is to relate incomplete data problems to complete data problems where estimation by maximum likelihood method is much simpler

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