Continuous time hidden Markov model for longitudinal data
文献类型：期刊论文
| 作者 | Zhou, Jie2; Song, Xinyuan3; Sun, Liuquan1
|
| 刊名 | JOURNAL OF MULTIVARIATE ANALYSIS
 |
| 出版日期 | 2020-09-01 |
| 卷号 | 179页码:16 |
| 关键词 | Continuous-time HMMs Longitudinal data ML estimator Unknown number of hidden states SCAD penalty |
| ISSN号 | 0047-259X |
| DOI | 10.1016/j.jmva.2020.104646 |
| 英文摘要 | Hidden Markov models (HMMs) describe the relationship between two stochastic processes, namely, an observed outcome process and an unobservable finite-state transition process. Given their ability to model dynamic heterogeneity, HMMs are extensively used to analyze heterogeneous longitudinal data. A majority of early developments in HMMs assume that observation times are discrete and regular. This assumption is often unrealistic in substantive research settings where subjects are intermittently seen and the observation times are continuous or not predetermined. However, available works in this direction restricted only to certain special cases with a homogeneous generating matrix for the Markov process. Moreover, early developments have mainly assumed that the number of hidden states of an HMM is fixed and predetermined based on the knowledge of the subjects or a certain criterion. In this article, we consider a general continuous-time HMM with a covariate specific generating matrix and an unknown number of hidden states. The proposed model is highly flexible, thereby enabling it to accommodate different types of longitudinal data that are regularly, irregularly, or continuously collected. We develop a maximum likelihood approach along with an efficient computer algorithm for parameter estimation. We propose a new penalized procedure to select the number of hidden states. The asymptotic properties of the estimators of the parameters and number of hidden states are established. Various satisfactory features, including the finite sample performance of the proposed methodology, are demonstrated through simulation studies. The application of the proposed model to a dataset of bladder tumors is presented. (C) 2020 Elsevier Inc. All rights reserved. |
| 资助项目 | National Natural Science Foundation of China[11671275] ; National Natural Science Foundation of China[11471223] ; National Natural Science Foundation of China[11771431] ; National Natural Science Foundation of China[11690015] ; National Natural Science Foundation of China[11926341] ; Key Laboratory of RCSDS, CAS, PR China[2008DP173182] ; Research Grants Council of the Hong Kong Special Administrative Region[14303017] ; Research Grants Council of the Hong Kong Special Administrative Region[14301918] ; Academy for Multidisciplinary Studies of Capital Normal University, PR China |
| WOS研究方向 | Mathematics |
| 语种 | 英语 |
| WOS记录号 | WOS:000552835600011 |
| 出版者 | ELSEVIER INC |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/51924]  |
| 专题 | 应用数学研究所 |
| 通讯作者 | Song, Xinyuan |
| 作者单位 | 1.Chinese Acad Sci, Acad Math & Syst Sci, Inst Appl Math, Beijing 100190, Peoples R China 2.Capital Normal Univ, Sch Math, Beijing 100048, Peoples R China 3.Chinese Univ Hong Kong, Dept Stat, Shatin, Hong Kong, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhou, Jie,Song, Xinyuan,Sun, Liuquan. Continuous time hidden Markov model for longitudinal data[J]. JOURNAL OF MULTIVARIATE ANALYSIS,2020,179:16. |
| APA | Zhou, Jie,Song, Xinyuan,&Sun, Liuquan.(2020).Continuous time hidden Markov model for longitudinal data.JOURNAL OF MULTIVARIATE ANALYSIS,179,16. |
| MLA | Zhou, Jie,et al."Continuous time hidden Markov model for longitudinal data".JOURNAL OF MULTIVARIATE ANALYSIS 179(2020):16. |
入库方式： OAI收割
来源：数学与系统科学研究院
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