English, Article edition: A copula-based Markov chain model for the analysis of binary longitudinal data Gabriel Escarela; Luis Carlos Perez-Ruiz; Russell Bowater

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Physical Description
  • article
Language
  • English

Edition details

Title
  • A copula-based Markov chain model for the analysis of binary longitudinal data
Author
  • Gabriel Escarela
  • Luis Carlos Perez-Ruiz
  • Russell Bowater
Physical Description
  • article
Notes
  • A fully parametric first-order autoregressive (AR(1)) model is proposed to analyse binary longitudinal data. By using a discretized version of a copula, the modelling approach allows one to construct separate models for the marginal response and for the dependence between adjacent responses. In particular, the transition model that is focused on discretizes the Gaussian copula in such a way that the marginal is a Bernoulli distribution. A probit link is used to take into account concomitant information in the behaviour of the underlying marginal distribution. Fixed and time-varying covariates can be included in the model. The method is simple and is a natural extension of the AR(1) model for Gaussian series. Since the approach put forward is likelihood-based, it allows interpretations and inferences to be made that are not possible with semi-parametric approaches such as those based on generalized estimating equations. Data from a study designed to reduce the exposure of children to the sun are used to illustrate the methods.
  • copula, discrete time series, Markov regression models, maximum likelihood, probit regression model, serial correlation,
  • RePEc:taf:japsta:v:36:y:2009:i:6:p:647-657
Language
  • English
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