Cluster‐Specific Variable Selection for Product Partition Models |
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| Authors: | Fernando A. Quintana Peter Müller Ana Luisa Papoila |
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| Affiliation: | 1. Departamento de Estadística, Facultad de MatemáticasPontificia Universidad Católica de Chile;2. Department of MathematicsThe University of Texas at Austin;3. CEAUL and Faculdade de Ciências Médicas da Universidade Nova de Lisboa |
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| Abstract: | 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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| Keywords: | clustering non‐parametric regression random partition model |
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