Hidden Markov Models With Applications in Cell Adhesion Experiments

• 成果类型: Article
• 署名作者: Hung, Ying; Wang, Yijie; Zarnitsyna, Veronika; Zhu, Cheng; Wu, C. F. Jeff
• 署名单位: Rutgers University System; Rutgers University New Brunswick; University System of Georgia; Georgia Institute of Technology; University System of Georgia; Georgia Institute of Technology
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2013.836973
• 发表日期: 2013
• 页码: 1469-1479
• 关键词: nonconcave penalized likelihood; maximum-likelihood; variable selection; time-series; ORDER; Poisson; Lasso

摘要

Estimation of the number of hidden states is challenging in hidden Markov models. Motivated by the analysis of a specific type of cell adhesion experiments, a new framework based on a hidden Markov model and double penalized order selection is proposed. The order selection procedure is shown to be consistent in estimating the number of states. A modified expectation-maximization algorithm is introduced to efficiently estimate parameters in the model. Simulations show that the proposed framework outperforms existing methods. Applications of the proposed methodology to real data demonstrate the accuracy of estimating receptor-ligand bond lifetimes and waiting times which are essential in kinetic parameter estimation. Supplementary materials for this article are available online.