Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.csda.2014.03.018
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. 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
URI: http://scholarbank.nus.edu.sg/handle/10635/125044
ISSN: 01679473
DOI: 10.1016/j.csda.2014.03.018
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

1
checked on Dec 11, 2018

WEB OF SCIENCETM
Citations

1
checked on Dec 11, 2018

Page view(s)

32
checked on Dec 7, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.