Probabilistic self-organizing maps for qualitative data
Neural Networks
Advances in Data Analysis and Classification
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Identifiability for a very flexible family of latent class models introduced recently is examined. These models allow for a conditional association between selected pairs of response variables conditionally on the latent and are based on logistic regression models both for the latent weights and for the conditional distributions of the response variables in terms of subject specific covariates. Generalized logits (global or continuation, which are relevant with ordered categorical responses and involve comparisons of cumulated probabilities) may be used as an alternative to the usual logits of type local which are log-linear. A compact matrix formulation for the Jacobian of the parametrization and a simple algorithm for checking local identifiability numerically is described. A few examples involving causal inference are examined.