TY - CHAP
TI - Label Switching Under Model Uncertainty
AB - Many useful methods have been developed to deal with label switching for finite mixtures where the number of components is known. In the first part of the talk, some of these methods will be reviewed. It is demonstrated for simulated as well as real data that post-processing of the MCMC draws by k-means clustering in the point process representation as suggested in the monograph of Frühwirth-Schnatter (2006) is a simple, yet useful tool of dealing with label switching.
In the second part of the talk, the role of the prior distribution in this context is investigated. It is shown that certain shrinkage priors help to discriminate between coefficients in the component specific parameter which are more or less homogenous and coefficients which are heterogeneous. This increases the chance of identifying a unique labelling, in particular, if only a few coefficients are relevant for heterogeneity.
The final part of the talk deals with label switching when the number of components is unknown. In particular, it is discussed how to handle the labelling problem for overfitting mixtures where it does not make sense to identify the components through a permutation of the component labels. It turns out that k-means clustering in the point process representation help to diagnose overfitting mixtures. Furthermore it provides information which components should be collapsed in order to identify the mixture.
AF - Workshop on Mixture Estimation and Applications
PP - Edinburgh
UR - http://www.icms.org.uk/workshops/mixture
PY - 2010-12-01
AU - Frühwirth-Schnatter, Sylvia
ER -