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Cluster-Specific Variable Selection for Product Partition Models

Author

Listed:
  • Fernando A. Quintana
  • Peter Müller
  • Ana Luisa Papoila

Abstract

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.

Suggested Citation

  • Fernando A. Quintana & Peter Müller & Ana Luisa Papoila, 2015. "Cluster-Specific Variable Selection for Product Partition Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(4), pages 1065-1077, December.
    • Handle: RePEc:bla:scjsta:v:42:y:2015:i:4:p:1065-1077
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    File URL: http://hdl.handle.net/10.1111/sjos.12151
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    References listed on IDEAS

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    1. Peter D. Hoff, 2005. "Subset Clustering of Binary Sequences, with an Application to Genomic Abnormality Data," Biometrics, The International Biometric Society, vol. 61(4), pages 1027-1036, December.
    2. Chung, Yeonseung & Dunson, David B., 2009. "Nonparametric Bayes Conditional Distribution Modeling With Variable Selection," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1646-1660.
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