Cluster‐Specific Variable Selection for Product Partition Models

type="main" xml:id="sjos12151-abs-0001"> 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.

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