On Recursive Estimation in Incomplete Data Models
作者:
Jian-Feng Yao,
期刊:
Statistics
(Taylor Available online 2000)
卷期:
Volume 34,
issue 1
页码: 27-51
ISSN:0233-1888
年代: 2000
DOI:10.1080/02331880008802704
出版商: Gordon & Breach Science Publishers
关键词: 62 F 12;62 L 20;Incomplete data;EM algorithm;recursive estimation;mixtures;stochastic algorithm
数据来源: Taylor
摘要:
We consider a new recursive algorithm for parameter estimation from an independent incomplete data sequence. The algorithm can be viewed as a recursive version of the well-known EM algorithm, augmented with a Monte-Carlo step which restores the missing data. Based on recent results on stochastic algorithms, we give conditions for the a.s. convergence of the algorithm. Moreover, asymptotical variance of this estimator is reduced by a simple averaging. Application to finite mixtures is given with a simulation experiment.
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