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

TL;DR

This paper introduces a unified EM-based approach for estimating Markov chain transition models from incomplete survey data, addressing issues like irregular sampling and subsampling in longitudinal studies.

Contribution

It adapts the EM algorithm for maximum likelihood estimation of Markov transition models using incomplete survey data, applicable to complex microsimulation models.

Findings

Effective estimation of transition models from incomplete data

Application to a simplified Future Elderly Model

Addresses irregular and subsampled survey data

Abstract

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