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A Markov chain model for longitudinal categorical data when there may be non-ignorable non-response

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  • Saling Huang
  • Morton Brown

Abstract

Longitudinal data with non-response occur in studies where the same subject is followed over time but data for each subject may not be available at every time point. When the response is categorical and the response at time t depends on the response at the previous time points, it may be appropriate to model the response using a Markov model. We generalize a second-order Markov model to include a non-ignorable non-response mechanism. Simulation is used to study the properties of the estimators. Large sample sizes are necessary to ensure that the algorithm converges and that the asymptotic properties of the estimators can be used.

Suggested Citation

  • Saling Huang & Morton Brown, 1999. "A Markov chain model for longitudinal categorical data when there may be non-ignorable non-response," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(1), pages 5-18.
    • Handle: RePEc:taf:japsta:v:26:y:1999:i:1:p:5-18
    DOI: 10.1080/02664769922610
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    References listed on IDEAS

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    1. Stasny, Elizabeth A, 1988. "Modeling Nonignorable Nonresponse in Categorical Panel Data with an Example in Estimating Gross Labor-Force Flows," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(2), pages 207-219, April.
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