Optimization via simulation for maximum likelihood estimation in incomplete data models

G. Fort; O. Cappe; E. Moulines; Ph Soulier

Optimization via simulation for maximum likelihood estimation in incomplete data models

G. Fort
, O. Cappe
, E. Moulines
, Ph Soulier

Telecom Paris

Research output: Contribution to conference › Paper › peer-review

1 Citation (Scopus)

Abstract

Optimization via simulation is a promising approach for solving maximum likelihood problems in incomplete data models. Among the techniques proposed to date, the Monte-Carlo EM algorithm (MCEM) proposed by Wei and Tanner has a strong potential but very few is known on its behavior and on strategies for monitoring its convergence. In this contribution, the convergence of MCEM is investigated with a particular emphasis on the stability issue (which is not guaranteed in the original algorithm described by Wei and Tanner). A random truncation strategy, inspired by the Chen's truncation method for stochastic approximation algorithms, is proposed and analyzed. Finally, the application of our results to blind estimation problems in which the complete data likelihood is from the exponential family is discussed.

Original language	English
Pages	80-83
Number of pages	4
Publication status	Published - 1 Dec 1998
Event	Proceedings of the 1998 9th IEEE SP Workshop on Statistical Signal and Array Processing - Portland, OR, USA Duration: 14 Sept 1998 → 16 Sept 1998

Conference

Conference	Proceedings of the 1998 9th IEEE SP Workshop on Statistical Signal and Array Processing
City	Portland, OR, USA
Period	14/09/98 → 16/09/98

Cite this

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title = "Optimization via simulation for maximum likelihood estimation in incomplete data models",

abstract = "Optimization via simulation is a promising approach for solving maximum likelihood problems in incomplete data models. Among the techniques proposed to date, the Monte-Carlo EM algorithm (MCEM) proposed by Wei and Tanner has a strong potential but very few is known on its behavior and on strategies for monitoring its convergence. In this contribution, the convergence of MCEM is investigated with a particular emphasis on the stability issue (which is not guaranteed in the original algorithm described by Wei and Tanner). A random truncation strategy, inspired by the Chen's truncation method for stochastic approximation algorithms, is proposed and analyzed. Finally, the application of our results to blind estimation problems in which the complete data likelihood is from the exponential family is discussed.",

author = "G. Fort and O. Cappe and E. Moulines and Ph Soulier",

year = "1998",

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note = "Proceedings of the 1998 9th IEEE SP Workshop on Statistical Signal and Array Processing ; Conference date: 14-09-1998 Through 16-09-1998",

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AU - Fort, G.

AU - Cappe, O.

AU - Moulines, E.

AU - Soulier, Ph

PY - 1998/12/1

Y1 - 1998/12/1

N2 - Optimization via simulation is a promising approach for solving maximum likelihood problems in incomplete data models. Among the techniques proposed to date, the Monte-Carlo EM algorithm (MCEM) proposed by Wei and Tanner has a strong potential but very few is known on its behavior and on strategies for monitoring its convergence. In this contribution, the convergence of MCEM is investigated with a particular emphasis on the stability issue (which is not guaranteed in the original algorithm described by Wei and Tanner). A random truncation strategy, inspired by the Chen's truncation method for stochastic approximation algorithms, is proposed and analyzed. Finally, the application of our results to blind estimation problems in which the complete data likelihood is from the exponential family is discussed.

AB - Optimization via simulation is a promising approach for solving maximum likelihood problems in incomplete data models. Among the techniques proposed to date, the Monte-Carlo EM algorithm (MCEM) proposed by Wei and Tanner has a strong potential but very few is known on its behavior and on strategies for monitoring its convergence. In this contribution, the convergence of MCEM is investigated with a particular emphasis on the stability issue (which is not guaranteed in the original algorithm described by Wei and Tanner). A random truncation strategy, inspired by the Chen's truncation method for stochastic approximation algorithms, is proposed and analyzed. Finally, the application of our results to blind estimation problems in which the complete data likelihood is from the exponential family is discussed.

M3 - Paper

AN - SCOPUS:0032299199

SP - 80

EP - 83

T2 - Proceedings of the 1998 9th IEEE SP Workshop on Statistical Signal and Array Processing

Y2 - 14 September 1998 through 16 September 1998

ER -

Optimization via simulation for maximum likelihood estimation in incomplete data models

Abstract

Conference

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