Estimating and interpolating a Markov chain from aggregate data

Abstract

Given aggregated longitudinal data generated by a Markov chain, which may be nonhomogeneous, the problem considered is that of modelling, estimating and interpolating the logarithms of partial odds and hence the transition probabilities. By partial odds is meant the probability of a transition to another state divided by the probability of no transition. A result establishing asymptotic normality leads to vector weighted least squares estimation of parameterised partial odds using standard regression methods. It is shown how to obtain estimates of one‐step transition probabilities from widely or irregularly spaced data. The methods are illustrated on an example concerning competing causes of death.