The use of a finite mixture of normal distributions in model-based clustering allows to
capture non-Gaussian data clusters. However, identifying the clusters from the normal components
is challenging and in general either achieved by imposing constraints on the model or
by using post-processing procedures.
Within the Bayesian framework we propose a different approach based on sparse finite
mixtures to achieve identifiability. We specify a hierarchical prior where the hyperparameters
are carefully selected such that they are reflective of the cluster structure aimed at. In addition,
this prior allows to estimate the model using standard MCMC sampling methods. In combination
with a post-processing approach which resolves the label switching issue and results in
an identified model, our approach allows to simultaneously (1) determine the number of clusters,
(2) flexibly approximate the cluster distributions in a semi-parametric way using finite
mixtures of normals and (3) identify cluster-specific parameters and classify observations. The
proposed approach is illustrated in two simulation studies and on benchmark data sets.
| Identifer | oai:union.ndltd.org:VIENNA/oai:epub.wu-wien.ac.at:5319 |
| Date | January 2017 |
| Creators | Malsiner-Walli, Gertraud, Frühwirth-Schnatter, Sylvia, Grün, Bettina |
| Publisher | Taylor & Francis |
| Source Sets | Wirtschaftsuniversität Wien |
| Language | English |
| Detected Language | English |
| Type | Article, PeerReviewed |
| Format | application/pdf |
| Rights | Creative Commons: Attribution 4.0 International (CC BY 4.0) |
| Relation | http://dx.doi.org/10.1080/10618600.2016.1200472, http://taylorandfrancis.com/, http://epub.wu.ac.at/5319/ |
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