A new approach to the analysis of heterogeneous categorical sequences is proposed. The first-order Markov model is employed in a finite mixture setting with initial state and transition probabilities being expressed as functions of time. The expectation–maximization algorithm approach to parameter estimation is implemented in the presence of positive equivalence constraints that determine which observations must be placed in the same class in the solution. The proposed model is applied to a dataset from the British Household Panel Survey to evaluate the association between the education background and life outcomes of study participants. The analysis of the survey data reveals many interesting relationships between the level of education and major life events.

提出了一种分析异构分类序列的新方法。一阶马尔可夫模型用于有限混合设置，其初始状态和跃迁概率表示为时间的函数。参数估计的期望最大化算法方法是在存在正等价约束的情况下实施的，该约束确定必须将哪些观测值放入解决方案的同一类中。拟议的模型应用于英国家庭调查的数据集，以评估受教育者的教育背景和生活成果之间的关联。对调查数据的分析揭示了教育水平与重大生活事件之间的许多有趣关系。