While advances continue to be made in model-based clustering, challenges
persist in modeling various data types such as panel data. Multivariate panel
data present difficulties for clustering algorithms because they are often
plagued by missing data and dropouts, presenting issues for estimation
algorithms. This research presents a family of hidden Markov models that
compensate for the issues that arise in panel data. A modified
expectation-maximization algorithm capable of handling missing not at random
data and dropout is presented and used to perform model estimation.