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Metropolis-Hastings Expectation Maximization Algorithm for Incomplete Data
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 Title & Authors
Metropolis-Hastings Expectation Maximization Algorithm for Incomplete Data
Cheon, Soo-Young; Lee, Hee-Chan;
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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.
Incomplete data;Expectation Maximization;Monte Carlo Expectation Maximization;Stochastic Expectation Maximization;Metropolis-Hastings Expectation Maximization;
 Cited by
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