621
Views
20
CrossRef citations to date
0
Altmetric
Original Articles

Bayesian Lasso-mixed quantile regression

&
Pages 868-880 | Received 13 Nov 2011, Accepted 16 Sep 2012, Published online: 12 Oct 2012

References

  • N. M., Laird, J. L., Ware, Random-effects models for longitudinal data. Biometrics, 38 (1982), 963–974. doi: 10.2307/2529876
  • AkaikeH. Information theory and an extension of maximum likelihood principle Second International Symposium on Information Theory PetrovB. N.CsakiF. Akademiai Kiado, Budapest 1973 267281
  • G., Schwarz, Estimating the dimension of a model. Ann. Statist., 6 (1978), 461–464. doi: 10.1214/aos/1176344136
  • Z., Chen, D., Dunson, Random effects selection in linear mixed models. Biometrics, 59 (2003), 762–769. doi: 10.1111/j.0006-341X.2003.00089.x
  • S. K., Kinney, D. B., Dunson, Fixed and random effects selection in linear and logistic models. Biometrics, 63 (2008), 690–698. doi: 10.1111/j.1541-0420.2007.00771.x
  • B. R., Saville, A. H., Herring, Testing random effects in the linear mixed model using approximate Bayes factors. Biometrics, 65 (2009), 369–376. doi: 10.1111/j.1541-0420.2008.01107.x. doi: 10.1111/j.1541-0420.2008.01107.x
  • H. D., Bondell, A., Krishna, S. K., Ghosh, Joint variable selection for fixed and random effects in linear mixed-effects models. Biometrics, 66 (2010), 69–77. doi: 10.1111/j.1541-0420.2010.01391.x
  • J. G., Ibrahim, H., Zhu, R. I., Garcia, R., Guo, Fixed and random effects selection in mixed effects models. Biometrics, 67 (2011), 495–503. doi: 10.1111/j.1541-0420.2010.01463.x
  • R., Koenker, Quantile Regression, Cambridge: Cambridge University Press 2005.
  • K., Yu, Z., Lu, J., Stander, Quantile regression: Applications and current research areas. Statistician, 52 (2003), 331–350.
  • R., Koenker, Bassett, G.Jr., Regression quantiles. Econometrica, 46 (1978), 33–50. doi: 10.2307/1913643
  • H., Zou, M., Yuan, Composite quantile regression and the oracle model selection theory. Ann. Statist., 36 (2008), 1108–1126. doi: 10.1214/07-AOS507
  • Y., Li, J., Zhu (2008). L1-norm quantile regression. J. Comput. Graph. Statist., 17, 163–185. doi: 10.1198/106186008X289155
  • Y., Wu, Y., Liu, Variable selection in quantile regression. Statist. Sin., 19 (2009), 801–817.
  • J., Bradic, J., Fan, W., Wang, Penalized composite quasi-likelihood for ultrahigh-dimensional variable selection. J. Roy. Statist. Soc., Ser., B 73 (2010), 325–349.
  • Q., Li, R., Xi, N., Lin, Bayesian regularized quantile regression. Bayesian Anal., 5 (2010), 1–24. doi: 10.1214/10-BA501
  • P. X., Chaili, Statistical modelling of age-related macular degeneration. A dissertation submitted to The University of Manchester for the degree of Master of Science in the Faculty of Engineering and Physical Sciences 2008
  • R., Koenker, J., Machado, Goodness of fit and related inference processes for quantile regression. J. Amer. Statist. Assoc., 94 (1999), 1296–1310. doi: 10.1080/01621459.1999.10473882
  • K., Yu, R. A., Moyeed, Bayesian quantile regression. Statist. Probab. Lett., 54 (2001), 437–447. doi: 10.1016/S0167-7152(01)00124-9
  • H., Kozumi, G., Kobayashi, Gibbs sampling methods for Bayesian quantile regression. J. Statist. Comput. Simul., 81 (2011), 1565–1578. doi: 10.1080/00949655.2010.496117
  • C., Reed, K., Yu, A partially collapsed Gibbs sampler for Bayesian quantile regression, Department of Mathematical Sciences, Brunel University. Tech. Rep. 2009
  • R., Alhamzawi, K., Yu, Conjugate priors and variable selection for Bayesian quantile regression. Comput. Statist. Data Anal., , in press. Available online 24 January 2012. 2012
  • D. F., Andrews, C. L., Mallows, Scale mixtures of normal distributions. J. Roy. Statist. Soc., Ser., B 36 (1974), 99–102.
  • T., Park, G., Casella (2008). The Bayesian Lasso. J. Amer. Statist. Assoc., 103, 681–686. doi: 10.1198/016214508000000337
  • R. S., Chhikara, L., Folks (1989). The Inverse Gaussian Distribution: Theory, Methodology, and Applications, New York: Marcel Dekker.
  • W., Bob (2009). SuppDists: Supplementary distributions. R package version 1.1-8
  • Y., Luo, H., Lian, M., Tian (2012). Bayesian quantile regression for longitudinal data model. J. Statist. Comput. Simul., 82, 1635–1649. doi: 10.1080/00949655.2011.590488
  • R., Alhamzawi, K., Yu, D. F., Benoit (2011). Bayesian adaptive LASSO quantile regression. Statist. Modelling, 12, 279–297. doi: 10.1177/1471082X1101200304
  • R., Koenker (2012). quantreg: Quantile Regression. R package version 4.79. Available at http://CRAN.R-project.org/package=quantreg.
  • D., Adler (2005). vioplot: A violin plot is a combination of a box plot and a kernel density plot. R package version 0.2. Available at http://cran.r-project.org/web/packages/vioplot/index.html.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.