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Continuous Time Hidden Markov Model for Longitudinal Data

发布时间：2018-03-16 作者： 浏览次数：
Speaker: 周洁 DateTime: 2018年3月17日（周六）下午3:30-4:30
Brief Introduction to Speaker:

周洁，首都师范大学。

 Place: 六号楼二楼报告厅
Abstract: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 are few and restricted only to certain special cases. In this article, we consider a general continuous-time HMM with 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.
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