%0 Journal Article %1 alhamzawi_bayesian_2012 %A Alhamzawi, Rahim %A Yu, Keming %A Benoit, Dries F. %D 2012 %J Statistical Modelling %K Bayesian Laplace Lasso, Quantile approximation, gibbs, inference, regression, regularization %N 3 %P 279--297 %R 10.1177/1471082X1101200304 %T Bayesian adaptive Lasso quantile regression %U http://smj.sagepub.com/content/12/3/279 %V 12 %X Recently, variable selection by penalized likelihood has attracted much research interest. In this paper, we propose adaptive Lasso quantile regression (BALQR) from a Bayesian perspective. The method extends the Bayesian Lasso quantile regression by allowing different penalization parameters for different regression coefficients. Inverse gamma prior distributions are placed on the penalty parameters. We treat the hyperparameters of the inverse gamma prior as unknowns and estimate them along with the other parameters. A Gibbs sampler is developed to simulate the parameters from the posterior distributions. Through simulation studies and analysis of a prostate cancer dataset, we compare the performance of the BALQR method proposed with six existing Bayesian and non-Bayesian methods. The simulation studies and the prostate cancer data analysis indicate that the BALQR method performs well in comparison to the other approaches.

@article{alhamzawi_bayesian_2012, abstract = {Recently, variable selection by penalized likelihood has attracted much research interest. In this paper, we propose adaptive Lasso quantile regression (BALQR) from a Bayesian perspective. The method extends the Bayesian Lasso quantile regression by allowing different penalization parameters for different regression coefficients. Inverse gamma prior distributions are placed on the penalty parameters. We treat the hyperparameters of the inverse gamma prior as unknowns and estimate them along with the other parameters. A Gibbs sampler is developed to simulate the parameters from the posterior distributions. Through simulation studies and analysis of a prostate cancer dataset, we compare the performance of the BALQR method proposed with six existing Bayesian and non-Bayesian methods. The simulation studies and the prostate cancer data analysis indicate that the BALQR method performs well in comparison to the other approaches.}, added-at = {2017-01-09T13:57:26.000+0100}, author = {Alhamzawi, Rahim and Yu, Keming and Benoit, Dries F.}, biburl = {https://www.bibsonomy.org/bibtex/254be1c763adf87c12f7da07653a1d8be/yourwelcome}, doi = {10.1177/1471082X1101200304}, interhash = {73de3a64acc2b3bdbf031cf123662e26}, intrahash = {54be1c763adf87c12f7da07653a1d8be}, issn = {1471-082X, 1477-0342}, journal = {Statistical Modelling}, keywords = {Bayesian Laplace Lasso, Quantile approximation, gibbs, inference, regression, regularization}, language = {en}, month = jun, number = 3, pages = {279--297}, timestamp = {2017-01-09T14:01:11.000+0100}, title = {Bayesian adaptive {Lasso} quantile regression}, url = {http://smj.sagepub.com/content/12/3/279}, urldate = {2013-01-29}, volume = 12, year = 2012 }