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Dendrogram of mixing measures: Hierarchical clustering and model selection for finite mixture models
The dendrogram's construction is derived from the theory of convergence of the mixing measures, and as a result, we can both consistently select the true number of mixing components and obtain the pointwise optimal convergence rate for parameter estimation from the tree, even when the model parameters are only weakly identifiable.
Strong identifiability and parameter learning in regression with heterogeneous response
We provide simulation studies and data illustrations, which shed some light on the parameter learning behavior found in several popular regression mixture models reported in the literature.
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