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|Title:||A Bayesian mixture of lasso regressions with t-errors|
|Keywords:||Mixture of regressions|
Particle Markov chain Monte Carlo
|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|
|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.|
|Source Title:||Computational Statistics and Data Analysis|
|Appears in Collections:||Staff Publications|
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