References
- Armagan, A. (2009), “Variational Bridge Regression,” in Artificial Intelligence and Statistics, Proceedings of Machine Learning Research (volume 5), eds. D. van Dyk and M. Welling, Clearwater Beach, FL, pp. 17–24.
- Armagan, A., Clyde, M., and Dunson, D. B. (2011), “Generalized Beta Mixtures of Gaussians,” in Advances in Neural Information Processing Systems, pp. 523–531.
- Armagan, A., Dunson, D. B., and Lee, J. (2013), “Generalized Double Pareto Shrinkage,” Statistica Sinica, 23, 119–143.
- Bhattacharya, A., Pati, D., Pillai, N. S., and Dunson, D. B. (2015), “Dirichlet–Laplace Priors for Optimal Shrinkage,” Journal of the American Statistical Association, 110, 1479–1490.
- Caron, F., and Doucet, A. (2008), “Sparse Bayesian Nonparametric Regression,” in Proceedings of the 25th International Conference on Machine Learning, ACM, pp. 88–95.
- Carvalho, C. M., Polson, N. G., and Scott, J. G. (2010), “The Horseshoe Estimator for Sparse Signals,” Biometrika, 97, 465–480.
- Gamerman, D., and Lopes, H. F. (2006), Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, Boca Raton, FL: CRC Press.
- Gramacy, R. B. (2017), “monomvn: Estimation for Multivariate Normal and Student-t Data With Monotone Missingness,” R Package Version 1.9-7.
- Griffin, J. E., and Brown, P. J. (2010), “Inference With Normal-Gamma Prior Distributions in Regression Problems,” Bayesian Analysis, 5, 171–188.
- ——— (2011), “Bayesian Hyper-LASSOs With Non-Convex Penalization,” Australian & New Zealand Journal of Statistics, 53, 423–442.
- Hahn, P. R., He, J., and Lopes, H. (2018), “Bayeslm: Efficient Sampling for Gaussian Linear Regression With Arbitrary Priors,” R Package Version 0.7.0.
- Hamermesh, D. S., and Parker, A. (2005), “Beauty in the Classroom: Instructors’ Pulchritude and Putative Pedagogical Productivity,” Economics of Education Review, 24, 369–376.
- Hans, C. (2009), “Bayesian Lasso Regression,” Biometrika, 96, 835–845.
- Johndrow, J. E., Orenstein, P., and Bhattacharya, A. (2017), “Scalable MCMC for Bayes shrinkage Priors,” arXiv preprint, arXiv:1705.00841.
- Murray, I., Adams, R. P., and MacKay, D. J. (2010), “Elliptical Slice Sampling,” in JMLR Workshop and Conference Proceedings (Vol. 9), JMLR, eds. Y. W. Teh and M. Titterington, Sardinia, Italy, pp. 541–548.
- Neal, R. M. (2003), “Slice Sampling,” Annals of Statistics, 31, 705–767.
- Neville, S. E., Ormerod, J. T., and Wand, M. (2014), “Mean Field Variational Bayes for Continuous Sparse Signal Shrinkage: Pitfalls and Remedies,” Electronic Journal of Statistics, 8, 1113–1151.
- Park, T., and Casella, G. (2008), “The Bayesian Lasso,” Journal of the American Statistical Association, 103, 681–686.
- Polson, N. G., and Scott, J. G. (2010), “Shrink Globally, Act Locally: Sparse Bayesian Regularization and Prediction,” in Bayesian Statistics (Vol. 9), eds. J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, A. F. M. Smith, and M. West, Oxford: Oxford University Press, pp. 501–538.
- Polson, N. G., Scott, J. G., and Windle, J. (2014), “The Bayesian Bridge,” Journal of the Royal Statistical Society, Series B, 76, 713–733.
- R Core Team (2017), R: A Language and Environment for Statistical Computing, Vienna, Austria: R Foundation for Statistical Computing.