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The Future Elderly Model and related microsimulations are modeled as Markovchains. These simulations rely on longitudinal survey data to estimate theirtransition models. The use of survey data presents several incomplete dataproblems, including coarse and irregular spacing of interviews, data collectionfrom subsamples, and structural changes to surveys over time. TheExpectation-Maximization algorithm is adapted to create a method for maximumlikelihood estimation of Markov chain transition models using incomplete data.The method is demonstrated on a simplified version of the Future Elderly Model.

Unified Method for Markov Chain Transition Model Estimation Using Incomplete Survey Data