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Identification of latent class Markov models with multiple indicators and correlated measurement errors

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A necessary condition for identification of latent class models is that the number of unknown independent parameters must not be greater than the number of observed cells in the contingency table. Such condition is not sufficient at all. Verifying Goodman’s sufficient condition for local identifiability may be, for complex models, a cumbersome procedure. In any case, local identifiability does not guarantee global identifability. The paper provides rules to ascertain global identifiability of some specifications of latent class Markov models, expressing the unknown parameters as a function of the observed frequencies. In the case that not all parameters of a model are identified, the outlined rules provide hints about the restrictions to impose in order to obtain fully identified models.

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Correspondence to Francesca Bassi.

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Bassi, F. Identification of latent class Markov models with multiple indicators and correlated measurement errors. J. Ital. Statist. Soc. 6, 201 (1997). https://doi.org/10.1007/BF03178912

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  • Latent class models
  • Global identifiability
  • Sufficient condition