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Article overview
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Variable Selection for Covariate Dependent Dirichlet Process Mixture of Regressions | William Barcella
; Maria De Iorio
; Gianluca Baio
; | Date: |
1 Aug 2015 | Abstract: | Dirichlet Process Mixture (DPM) models have been increasingly employed to
specify random partition models that take into account possible patterns within
the covariates. Furthermore, in response to large numbers of covariates,
methods for selecting the most important covariates have been proposed.
Commonly, the covariates are chosen either for their importance in determining
the clustering of the observations or for their effect on the level of a
response variable (in case a regression model is specified). Typically both
strategies involve the specification of latent indicators that regulate the
inclusion of the covariates in the model. Common examples involve the use of
spike and slab prior distributions. In this work we review the most relevant
DPM models that include the covariate information in the induced partition of
the observations and we focus extensively on available variable selection
techniques for these models. We highlight the main features of each model and
demonstrate them in simulations. | Source: | arXiv, 1508.0129 | Services: | Forum | Review | PDF | Favorites |
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