Process data, which are temporally ordered sequences of categorical observations, are of recent interest due to its increasing abundance and the desire to extract useful information. A process is a collection of time-stamped events of different types, recording how an individual behaves in a given time period. The process data are too complex in terms of size and irregularity for the classical psychometric models to be directly applicable and, consequently, new ways for modeling and analysis are desired. We introduce herein a latent theme dictionary model for processes that identifies co-occurrent event patterns and individuals with similar behavioral patterns. Theoretical properties are established under certain regularity conditions for the likelihood-based estimation and inference. A nonparametric Bayes algorithm using the Markov Chain Monte Carlo method is proposed for computation. Simulation studies show that the proposed approach performs well in a range of situations. The proposed method is applied to an item in the 2012 Programme for International Student Assessment with interpretable findings.

中文翻译：

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用于在过程数据中寻找共现模式的潜在主题词典模型

过程数据是按时间顺序排列的分类观察序列，由于其数量不断增加和提取有用信息的愿望，最近引起了人们的兴趣。流程是不同类型的带时间戳的事件的集合，记录个人在给定时间段内的行为。就经典心理测量模型而言，过程数据在大小和不规则性方面过于复杂，无法直接应用，因此需要新的建模和分析方法。我们在此引入了一个潜在主题字典模型，用于识别同时发生的事件模式和具有相似行为模式的个人。基于似然的估计和推理的理论属性是在一定的规律性条件下建立的。提出了一种使用马尔可夫链蒙特卡罗方法的非参数贝叶斯算法进行计算。仿真研究表明，所提出的方法在各种情况下都表现良好。建议的方法应用于 2012 年国际学生评估计划中的一个项目，结果可解释。