Statistics Seminar（2016-18）

Topic:Model Selection in Hidden Markov Models via Marginal Likelihood

Speaker:Cheng-Der Fuh, National Central University

Time:Wednesday, 14 September, 14:00-15:00

Place:Room 216, Guanghua Building 2

Abstract:

Hidden Markov models (HMM) has been widely adopted by scientists from various fields for modeling stochastic processes with Markovian structures, i.e. the underlying process is a discrete Markov chain and the observations are noisy realizations of the underlying process. Determining the number of hidden states for an HMM is a model selection problem which has yet to be satisfactorily solved, especially for the highly used Gaussian HMM with heterogenous covariance. In this paper, we propose an asymptotically consistent method for determining the number of hidden states of HMM based on the marginal likelihood, which is obtained by integrating out both the parameters and hidden states. We give a rigorous proof of the asymptotic consistency of the proposed method and provide simulation studies and real data examples to compare the proposed method with the currently mostly adopted method, the Bayesian information criterion (BIC). We have also developed an R package `HMMselect' based on the proposed algorithm.

Introduction:

Dr. Cheng-Der Fuh received his B.S. in Mathematics, from National Central University, Taiwan, in 1980, and his Ph.D. in Statistics and Mathematics, from Iowa State University, Iowa, USA, in 1989. After graduation, he joined Academia Sinica in Taiwan. Now, he is a chair professor at National Central University, where he has worked since 2006. His research interests are generally in the areas of statistics, applied probability, quantitative finance and signal processing.

//www.stat.ncu.edu.tw/index.php/eng/faculty/more/89

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