期刊
PSYCHOMETRIKA
卷 81, 期 1, 页码 102-134出版社
SPRINGER
DOI: 10.1007/s11336-014-9431-z
关键词
differential equation; dynamic; nonlinear; stochastic EM; longitudinal
类别
资金
- NSF [BCS-0826844]
- NIH [RR025747-01, P01CA142538-01, MH086633, EB005149-01, AG033387, R01GM105004]
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [1407655] Funding Source: National Science Foundation
The past decade has evidenced the increased prevalence of irregularly spaced longitudinal data in social sciences. Clearly lacking, however, are modeling tools that allow researchers to fit dynamic models to irregularly spaced data, particularly data that show nonlinearity and heterogeneity in dynamical structures. We consider the issue of fitting multivariate nonlinear differential equation models with random effects and unknown initial conditions to irregularly spaced data. A stochastic approximation expectation-maximization algorithm is proposed and its performance is evaluated using a benchmark nonlinear dynamical systems model, namely, the Van der Pol oscillator equations. The empirical utility of the proposed technique is illustrated using a set of 24-h ambulatory cardiovascular data from 168 men and women. Pertinent methodological challenges and unresolved issues are discussed.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据