Semiparametric hidden Markov model with non-parametric regression | |
Huang, Mian1; Ji, Qinghua1; Yao, Weixin2 | |
刊名 | COMMUNICATIONS IN STATISTICS-THEORY AND METHODS |
2018 | |
卷号 | 47期号:21页码:5196-5204 |
关键词 | EM algorithm forward-backward algorithm hidden Markov model regression kernel regression |
ISSN号 | 0361-0926 |
DOI | 10.1080/03610926.2017.1388398 |
英文摘要 | The hidden Markov model regression (HMMR) has been popularly used in many fields such as gene expression and activity recognition. However, the traditional HMMR requires the strong linearity assumption for the emission model. In this article, we propose a hidden Markov model with non-parametric regression (HMM-NR), where the mean and variance of emission model are unknown smooth functions. The new semiparametric model might greatly reduce the modeling bias and thus enhance the applicability of the traditional hidden Markov model regression. We propose an estimation procedure for the transition probability matrix and the non-parametric mean and variance functions by combining the ideas of the EM algorithm and the kernel regression. Simulation studies and a real data set application are used to demonstrate the effectiveness of the new estimation procedure. |
WOS研究方向 | Mathematics |
语种 | 英语 |
出版者 | TAYLOR & FRANCIS INC |
WOS记录号 | WOS:000441632900003 |
内容类型 | 期刊论文 |
源URL | [http://10.2.47.112/handle/2XS4QKH4/746] |
专题 | 上海财经大学 |
通讯作者 | Yao, Weixin |
作者单位 | 1.Shanghai Univ Finance & Econ, Sch Stat & Management, Shanghai, Peoples R China; 2.Univ Calif Riverside, Dept Stat, Riverside, CA 92521 USA |
推荐引用方式 GB/T 7714 | Huang, Mian,Ji, Qinghua,Yao, Weixin. Semiparametric hidden Markov model with non-parametric regression[J]. COMMUNICATIONS IN STATISTICS-THEORY AND METHODS,2018,47(21):5196-5204. |
APA | Huang, Mian,Ji, Qinghua,&Yao, Weixin.(2018).Semiparametric hidden Markov model with non-parametric regression.COMMUNICATIONS IN STATISTICS-THEORY AND METHODS,47(21),5196-5204. |
MLA | Huang, Mian,et al."Semiparametric hidden Markov model with non-parametric regression".COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 47.21(2018):5196-5204. |
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