Double Dirichlet process mixtures
Seminar Room 1, Newton Institute
In this work we consider a new class of Dirichlet process mixtures, that we call the double and multple DPM class, which generates a clustering structure in the data that is different from those generated by simple DPM or other DPM models. Fitting of double and related DPM models is possible by MCMC methods by multiple applications of the standard Polya urn and blocked Gibbs samplers within each sweep of the sampling. Based on experimental investigations we show that the proposed model performs reasonably well when the model is correctly specified and when the model is misspecified. We also investigate the similarity between the clustering produced by the model fit and the true clustering. Finally, we consider model comparison and model diagnostics, and illustrate the implementation, performance and applicability of the proposed class of DPM models in regressions for survival data and clustered longitudinal data.