Please use this identifier to cite or link to this item:
https://doi.org/10.1016/j.csda.2014.03.018
DC Field | Value | |
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dc.title | A Bayesian mixture of lasso regressions with t-errors | |
dc.contributor.author | Cozzini, A. | |
dc.contributor.author | Jasra, A. | |
dc.contributor.author | Montana, G. | |
dc.contributor.author | Persing, A. | |
dc.date.accessioned | 2016-06-02T10:30:09Z | |
dc.date.available | 2016-06-02T10:30:09Z | |
dc.date.issued | 2014 | |
dc.identifier.citation | Cozzini, A., Jasra, A., Montana, G., Persing, A. (2014). A Bayesian mixture of lasso regressions with t-errors. Computational Statistics and Data Analysis. ScholarBank@NUS Repository. https://doi.org/10.1016/j.csda.2014.03.018 | |
dc.identifier.issn | 01679473 | |
dc.identifier.uri | http://scholarbank.nus.edu.sg/handle/10635/125044 | |
dc.description.abstract | The following article considers a mixture of regressions with variable selection problem. In many real-data scenarios, one is faced with data which possess outliers, skewness and, simultaneously, one would like to be able to construct clusters with specific predictors that are fairly sparse. A Bayesian mixture of lasso regressions with t-errors to reflect these specific demands is developed. The resulting model is necessarily complex and to fit the model to real data, a state-of-the-art Particle Markov chain Monte Carlo (PMCMC) algorithm based upon sequential Monte Carlo (SMC) methods is developed. The model and algorithm are investigated on both simulated and real data. © 2014 Elsevier B.V. All rights reserved. | |
dc.description.uri | http://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.csda.2014.03.018 | |
dc.source | Scopus | |
dc.subject | Mixture of regressions | |
dc.subject | Particle Markov chain Monte Carlo | |
dc.subject | Variable selection | |
dc.type | Article | |
dc.contributor.department | STATISTICS & APPLIED PROBABILITY | |
dc.description.doi | 10.1016/j.csda.2014.03.018 | |
dc.description.sourcetitle | Computational Statistics and Data Analysis | |
dc.description.coden | CSDAD | |
dc.identifier.isiut | 000337869500007 | |
Appears in Collections: | Staff Publications |
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