Bayesian analysis for zero-or-one inflated proportion data using quantile regression

References

• Ferrari S, Cribari-Neto F. Beta regression for modelling rates and proportions. J Appl Stat. 2004;31:799–815. doi: 10.1080/0266476042000214501
   Web of Science ®Google Scholar
• Simas AB, Barreto-Souza W, Rocha AV. Improved estimators for a general class of beta regression models. Comput Statist Data Anal. 2010;54:348–366. doi: 10.1016/j.csda.2009.08.017
   Web of Science ®Google Scholar
• Ospina R, Ferrari SL. A general class of zero-or-one inflated beta regression models. Comput Stat Data Anal. 2012;56:1609–1623. doi: 10.1016/j.csda.2011.10.005
   Web of Science ®Google Scholar
• Cragg J. Some statistical models for limited dependent variables with application to the demand for durable goods. Econometrica. 1971;39:829–844. doi: 10.2307/1909582
   Web of Science ®Google Scholar
• Diebolt J, Robert C. Estimation of finite mixture distributions through Bayesian sampling. J R Stat Soc Ser B Stat Methodol. 1994;56:363–375.
   Web of Science ®Google Scholar
• Koenker R, Bassett G. Regression quantiles. Econometrica. 1978;46:33–50. doi: 10.2307/1913643
   Web of Science ®Google Scholar
• Yu K, Lu Z, Stander J. Quantile regression: applications and current research areas. The Statistician. 2003;53:331–350.
   Google Scholar
• Koenker R. Quantile regression. Cambridge: Cambridge University Press; 2005.
   Google Scholar
• Elsner JB, Kossin JP, Jagger TH. The increasing intensity of the strongest tropical cyclones. Nature. 2008;455:92–95. doi: 10.1038/nature07234
   PubMed Web of Science ®Google Scholar
• Yu K, Moyeed R. Bayesian quantile regression. Statist Probab Lett. 2001;54:437–447. doi: 10.1016/S0167-7152(01)00124-9
   Web of Science ®Google Scholar
• Kozumi H, Kobayashi G. Gibbs sampling methods for Bayesian quantile regression. J Stat Comput Simul. 2011;81:1565–1578. doi: 10.1080/00949655.2010.496117
   Web of Science ®Google Scholar
• Lum K, Gelfand A. Spatial quantile multiple regression using the asymmetric laplace process. Bayesian Anal. 2012;7:1–24. doi: 10.1214/12-BA701
   Web of Science ®Google Scholar
• Ji Y, Lin N, Zhang B. Model selection in binary and Tobit quantile regression using the Gibbs sampler. Comput Statist Data Anal. 2012;56:827–839. doi: 10.1016/j.csda.2011.10.003
   Web of Science ®Google Scholar
• Alhamzawi R, Yu K. Variable selection in quantile regression via Gibbs sampling. J Appl Stat. 2012;39:799–813. doi: 10.1080/02664763.2011.620082
   Web of Science ®Google Scholar
• Alhamzawi R, Yu K. Conjugate priors and variable selection for Bayesian quantile regression. Computat Statist Data Anal. 2013;64:209–219. doi: 10.1016/j.csda.2012.01.014
   Web of Science ®Google Scholar
• Luo Y, Lian H, Tian M. Bayesian quantile regression for longitudinal data models. J Statist Comput Simul. 2012;82:1635–1649. doi: 10.1080/00949655.2011.590488
   Web of Science ®Google Scholar
• Koenker R, Machado J. Goodness of fit and related inference processes for quantile regression. J Amer Statist Assoc. 1999;94:1296–1310. doi: 10.1080/01621459.1999.10473882
   Web of Science ®Google Scholar
• Tanner M, Wong W. The calculation of posterior distributions by data augmentation. J Amer Statist Assoc. 1987;82:528–540. doi: 10.1080/01621459.1987.10478458
   Web of Science ®Google Scholar
• Khare K, Hobert JP. Geometric ergodicity of the Gibbs sampler for Bayesian quantile regression. J Multivariate Anal. 2012;112:108–116. doi: 10.1016/j.jmva.2012.05.004
   Web of Science ®Google Scholar
• Yu K, Chen CW, Reed C, Dunson DB. Bayesian variable selection in quantile regression. Stat Interface. 2013;6:261–274. doi: 10.4310/SII.2013.v6.n2.a9
   Web of Science ®Google Scholar
• Sriram K, Ramamoorthi R, Ghosh P. Posterior consistency of Bayesian quantile regression based on the misspecified asymmetric laplace density. Bayesian Anal. 2013;8:479–504. doi: 10.1214/13-BA817
   Web of Science ®Google Scholar
• Barndorff-Nielsen O, Shephard N. Non-Gaussian Ornstein-Uhlenbeck-based models and some of their uses in financial economics. J R Stat Soc Ser B Stat Methodol. 2001;63:167–241. doi: 10.1111/1467-9868.00282
   Web of Science ®Google Scholar
• Gelman A, Carlin JB, Stern HS, Rubin DB. Bayesian data analysis. London: CRC Press; 2003.
   Google Scholar
• Koenker R. Quantreg: quantile regression. R package version 5.05; 2013. Available from: http://CRAN.R-project.org/package=quantreg
   Google Scholar
• The Secretary General's advisory group on Energy and Climate Change. Energy for a Sustainable Future. New York: United Nations, 2010. Available from: http://www.un.org/millenniumgoals/pdf/AGECCsummaryreport[1].pdf.
   Google Scholar
• Benoit D, Van den Poel D. Binary quantile regression: a Bayesian approach based on the asymmetric Laplace distribution. J Appl Econometrics. 2012;27:1174–1188. doi: 10.1002/jae.1216
   Web of Science ®Google Scholar