## Abstract

The expectation–maximization (EM) algorithm is widely used in computing the maximum likelihood estimates when the observations can be viewed as incomplete data. However, the convergence rate of the EM algorithm can be slow especially when a large portion of the data is missing. We propose the multiset EM algorithm that can help the convergence of the EM algorithm. The key idea is to augment the system with a multiset of the missing component, and construct an appropriate joint distribution of the augmented complete data. We demonstrate that the multiset EM algorithm can outperform the EM algorithm, especially when EM has difficulties in convergence and the E-step involves Monte Carlo approximation.

Original language | English (US) |
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Pages (from-to) | 41-48 |

Number of pages | 8 |

Journal | Statistics and Probability Letters |

Volume | 126 |

DOIs | |

State | Published - Jul 1 2017 |

## Keywords

- Data augmentation
- EM algorithm
- Incomplete data
- Multiset

## ASJC Scopus subject areas

- Statistics and Probability
- Statistics, Probability and Uncertainty