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首页> 外文期刊>Psychometrika >Zero-Inflated Regime-Switching Stochastic Differential Equation Models for Highly Unbalanced Multivariate, Multi-Subject Time-Series Data
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Zero-Inflated Regime-Switching Stochastic Differential Equation Models for Highly Unbalanced Multivariate, Multi-Subject Time-Series Data

机译：用于高度不平衡多变量的零充气的方案切换随机微分方程模型，多对象时间序列数据

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摘要

In the study of human dynamics, the behavior under study is often operationalized by tallying the frequencies and intensities of a collection of lower-order processes. For instance, the higher-order construct of negative affect may be indicated by the occurrence of crying, frowning, and other verbal and nonverbal expressions of distress, fear, anger, and other negative feelings. However, because of idiosyncratic differences in how negative affect is expressed, some of the lower-order processes may be characterized by sparse occurrences in some individuals. To aid the recovery of the true dynamics of a system in cases where there may be an inflation of such zero responses, we propose adding a regime (unobserved phase) of non-occurrence to a bivariate Ornstein-Uhlenbeck (OU) model to account for the high instances of non-occurrence in some individuals while simultaneously allowing for multivariate dynamic representation of the processes of interest under nonzero responses. The transition between the occurrence (i.e., active) and non-occurrence (i.e., inactive) regimes is represented using a novel latent Markovian transition model with dependencies on latent variables and person-specific covariates to account for inter-individual heterogeneity of the processes. Bayesian estimation and inference are based on Markov chain Monte Carlo algorithms implemented using the JAGS software. We demonstrate the utility of the proposed zero-inflated regime-switching OU model to a study of young children's self-regulation at 36 and 48months.
机译：在人类动态的研究中，通过将频率和强度的收集的较低过程的频率和强度进行了频繁，研究了研究的行为。例如，可能会通过哭泣，皱眉和其他痛苦，恐惧，愤怒和其他负面情绪的悲伤，皱眉和非语言和非语言表达的痛苦，恐惧和愤怒和其他负面情绪的高阶构建负面影响。然而，由于表达了负面影响的特质差异，一些较低过程可以通过一些个体的稀疏发生来表征。为了帮助恢复系统的真实动态，在可能存在这种零响应的情况下，我们建议将不发生的政权（未观察到的阶段）添加到一分匹瓦斯特·uhlenbeck（OU）模型来解释在一些人的高度不发生的高实例同时允许在非零响应下的感兴趣过程的多变量动态表示。使用新的潜在马洛维亚转换模型来表示发生（即，活跃的）和非发生（即，非活动）和不发生（即非活动）制度之间的转换，该转换模型具有潜在变量的依赖性和特定于人格的协变量，以考虑过程的间间异质性。贝叶斯估计和推论基于使用JAGS软件实现的Markov Chain Monte Carlo算法。我们展示了提议的零充气制度切换的OU模型在36和48个月的幼儿自我监管研究中的效用。

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