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Multivariate bounded discrete data arises in many fields. In the setting oflongitudinal dementia studies, such data is collected when individuals completeneuropsychological tests. We outline a modeling and inference procedure thatcan model the joint distribution conditional on baseline covariates, leveragingprevious work on mixtures of experts and latent class models. Furthermore, weillustrate how the work can be extended when the outcome data is missing atrandom using a nested EM algorithm. The proposed model can incorporatecovariate information, perform imputation and clustering, and infer latenttrajectories. We apply our model on simulated data and an Alzheimer's diseasedata set.

Modeling Missing at Random Neuropsychological Test Scores Using a Mixture of Binomial Product Experts