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Cluster‐Specific Variable Selection for Product Partition Models
Author(s) -
Quintana Fernando A.,
Müller Peter,
Papoila Ana Luisa
Publication year - 2015
Publication title -
scandinavian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.359
H-Index - 65
eISSN - 1467-9469
pISSN - 0303-6898
DOI - 10.1111/sjos.12151
Subject(s) - covariate , mathematics , homogeneity (statistics) , partition (number theory) , statistics , cluster (spacecraft) , econometrics , model selection , regression analysis , homogeneous , regression , computer science , combinatorics , programming language
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.