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Title: A Bayesian mixture of lasso regressions with t-errors
Authors: Cozzini, A.
Jasra, A. 
Montana, G.
Persing, A.
Keywords: Mixture of regressions
Particle Markov chain Monte Carlo
Variable selection
Issue Date: 2014
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.
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
ISSN: 01679473
DOI: 10.1016/j.csda.2014.03.018
Appears in Collections:Staff Publications

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