Multivariate bounded discrete data arises in many fields. In the setting of dementia studies, such data is collected when individuals complete neuropsychological tests. We outline a modeling and inference procedure that can model the joint distribution conditional on baseline covariates, leveraging previous work on mixtures of experts and latent class models. Furthermore, we illustrate how the work can be extended when the outcome data is missing at random using a nested EM algorithm. The proposed model can incorporate covariate information and perform imputation and clustering. We apply our model on simulated data and an Alzheimer's disease data set.

翻译：多元有界离散数据广泛出现于多个研究领域。在痴呆症研究场景中，此类数据通过个体完成神经心理学测试收集。本文基于专家混合与潜在类别模型的现有研究，提出一种能够以基线协变量为条件对联合分布进行建模的建模与推断框架。进一步地，我们通过嵌套EM算法展示了该方法如何扩展至结局数据随机缺失的情形。所提出的模型能够整合协变量信息，并执行数据插补与聚类分析。我们在模拟数据和阿尔茨海默病数据集上验证了该模型的有效性。