  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 due to the unique
correlation structure, a consequence of taking observations on several subjects
over multiple time points. Additionally, panel data 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
unique correlation structures 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.
