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http://dx.doi.org/10.5351/KJAS.2012.25.1.183

Metropolis-Hastings Expectation Maximization Algorithm for Incomplete Data  

Cheon, Soo-Young (Department of Informational Statistics, Korea University)
Lee, Hee-Chan (Department of Economics and Statistics, Korea University)
Publication Information
The Korean Journal of Applied Statistics / v.25, no.1, 2012 , pp. 183-196 More about this Journal
Abstract
The inference for incomplete data such as missing data, truncated distribution and censored data is a phenomenon that occurs frequently in statistics. To solve this problem, Expectation Maximization(EM), Monte Carlo Expectation Maximization(MCEM) and Stochastic Expectation Maximization(SEM) algorithm have been used for a long time; however, they generally assume known distributions. In this paper, we propose the Metropolis-Hastings Expectation Maximization(MHEM) algorithm for unknown distributions. The performance of our proposed algorithm has been investigated on simulated and real dataset, KOSPI 200.
Keywords
Incomplete data; Expectation Maximization; Monte Carlo Expectation Maximization; Stochastic Expectation Maximization; Metropolis-Hastings Expectation Maximization;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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