Estimating and interpolating a Markov chain from aggregate data

B. A. Davis*, C. R. Heathcote, T. J. O'Neill

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    13 Citations (Scopus)

    Abstract

    Given aggregated longitudinal data generated by a Markov chain, which may be non-homogeneous, 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.

    Original languageEnglish
    Pages (from-to)95-110
    Number of pages16
    JournalBiometrika
    Volume89
    Issue number1
    DOIs
    Publication statusPublished - 2002

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