TY - JOUR
T1 - A Bayesian mixture of lasso regressions with t-errors
AU - Cozzini, Alberto
AU - Jasra, Ajay
AU - Montana, Giovanni
AU - Persing, Adam
N1 - Generated from Scopus record by KAUST IRTS on 2019-11-20
PY - 2014/1/1
Y1 - 2014/1/1
N2 - 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.
AB - 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.
UR - https://linkinghub.elsevier.com/retrieve/pii/S0167947314000954
UR - http://www.scopus.com/inward/record.url?scp=84901950529&partnerID=8YFLogxK
U2 - 10.1016/j.csda.2014.03.018
DO - 10.1016/j.csda.2014.03.018
M3 - Article
SN - 0167-9473
VL - 77
JO - Computational Statistics and Data Analysis
JF - Computational Statistics and Data Analysis
ER -