Penalized composite likelihood estimation for hidden Markov models with unknown number of states

被引：0

作者：

Lin, Yong ^{[1
]}

Huang, Mian ^{[1
]}

机构：

[1] Shanghai Univ Finance & Econ, Sch Stat & Management, Shanghai 200433, Peoples R China

来源：

STATISTICS & PROBABILITY LETTERS | 2025年 / 216卷

关键词：

Hidden Markov models; Order selection; Penalized composite likelihood; EM algorithm; FINITE MIXTURE-MODELS; CONVERGENCE;

D O I：

10.1016/j.spl.2024.110247

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Estimating hidden Markov models (HMMs) with unknown number of states is a challenging task. In this paper, we propose a new penalized composite likelihood approach for simultaneously estimating both the number of states and the parameters in an overfitted HMM. We prove the order selection consistency and asymptotic normality of the resultant estimator. Simulation studies and an application demonstrate the finite sample performance of the proposed method.

引用

页数：4

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