[预告]加拿大不列颠哥伦比亚大学统计系陈家骅教授专题讲座
发布日期: 2013-06-18   浏览次数:

 
应国民核算研究院邀请,加拿大不列颠哥伦比亚大学统计系陈家骅教授,将来我院为我院师生作专题报告。具体安排如下:
讲座主题COMPOSITE LIKELIHOOD UNDER HIDDEN MARKOV MODEL
讲座安排2013620日(周四)上午930-11:30  京师大厦三层第二会议室
主讲人: 陈家骅
主讲人简介
Canada Research Chair (Tier I, 2007-2013)., Department of Statistics, University of British Columbia; Fields of Interest:Finite Mixture Models, Statistical Genetics, Variable Selection, Empirical Likelihood ,Sampling Survey, Asymptotic Theory .
Personal Websitehttp://www.stat.ubc.ca/~jhchen/
讲座摘要(Abstract:
This paper proposes a composite likelihood approaches an alternative to the full likelihood approach for the analysis of time series data from hidden Markov models. The proposed method requires correctly specifying only the joint density of pairs of consecutive observations. Hence, the proposed composite likelihood is algebraically simpler than the corresponding full likelihood while it retains the crucial information on transition probabilities. The proposed maximum composite likelihood estimator with a regularization term added to the composite likelihood is consistent, asymptotically normal, and easy to implement.  This estimator overcomes a difficulty in maximum likelihood estimation: both the full and composite likelihoods are unbounded when the kernel distribution is normal. Our simulation studies show that the new estimator is highly efficient and robust. We apply the method to a time series for the USD/GBP exchange rate under a two-state hidden Markov model, as suggested by Engel and Hamilton (1990). The composite likelihood approach is more robust for inference and has better in-sample and out-of-sample performance than the full likelihood.

 

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