Barcella, W;
Iorio, MD;
Baio, G;
(2015)
Variable Selection for Covariate Dependent Dirichlet Process Mixtures of Regressions.
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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.
| Type: | Working / discussion paper |
|---|---|
| Title: | Variable Selection for Covariate Dependent Dirichlet Process Mixtures of Regressions |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | http://arxiv.org/abs/1508.00129v2 |
| Language: | English |
| Additional information: | 26 pages, 8 figures |
| Keywords: | stat.AP, stat.AP |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1470381 |
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