Dec 11, 2014 · A latent Gaussian mixture model to classify ordinal data is proposed. The observed categorical variables are considered as a discretization ...
A latent Gaussian mixture model to classify ordinal data is proposed. The observed categorical variables are considered as a discretization of an underlying ...
A latent Gaussian mixture model to classify ordinal data is proposed that allows us to overcome the computational problems arising in the full maximum ...
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Data generated from a latent two-component mixture model; 5 ordinal variables with 5 categories; three of them are less informative about the cluster structure.
Preliminary estimators for a mixture model of ordinal data - SpringerLink
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Aug 23, 2012 · In this paper, we propose preliminary estimators for the parameters of a mixture distribution introduced for the analysis of ordinal data ...
Another advantage of GCM is that it can easily be extended to mixed data with ordinal and continuous variables. The conditional GCM was introduced by Anderson ...
Apr 11, 2015 · Mixture models for ordinal data: a pairwise likelihood approach · Computer Science, Mathematics. Statistics and Computing · 2014.
A mixture model is considered to classify continuous and/or ordinal variables. Under this model, both the continuous and the ordinal variables are assumed ...
Apr 16, 2012 · Pairwise maximum likelihood (PML) estimation method is developed for factor analysis models with ordinal data.
A pairwise maximum likelihood (PML) estimation method is developed for factor analysis models with ordinal data and fitted both in an exploratory and ...