Clustering data with non-ignorable missingness using semi-parametric mixture models assuming independence within components

We propose a semi-parametric clustering model assuming conditional independence given the component. One advantage is that this model can handle non-ignorable missingness. The model defines each component as a product of univariate probability distributions but makes no assumption on the form of eac...

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