Cluster-Specific Variable Selection for Product Partition Models

Fernando A. Quintana, Peter Müller, Ana Luisa Papoila

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


We propose a random partition model that implements prediction with many candidate covariates and interactions. The model is based on a modified product partition model that includes a regression on covariates by favouring homogeneous clusters in terms of these covariates. Additionally, the model allows for a cluster-specific choice of the covariates that are included in this evaluation of homogeneity. The variable selection is implemented by introducing a set of cluster-specific latent indicators that include or exclude covariates. The proposed model is motivated by an application to predicting mortality in an intensive care unit in Lisboa, Portugal.

Original languageEnglish
Pages (from-to)1065-1077
Number of pages13
JournalScandinavian Journal of Statistics
Issue number4
Publication statusPublished - 1 Dec 2015


  • Clustering
  • Non-parametric regression
  • Random partition model


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