References
- Akaike, H. 1973. Information theory and an extension of the maximum likelihood principle. In Second International Symposium on Information Theory, ed. Petrov, B. N. and F. Csaki, 267–81. Budapest: Akademiai Kiado. (Reproduced in Breakthroughs in Statistics, Volume1, S. Kotz, and N. L. Johnson, eds., Springer Verlag, New York, 1992).
- Andrews, D.F., and C. L. Mallows. 1974. Scale mixtures of normal distributions. Journal of the Royal Statistical Society, Series B, 36:99–102.
- Chen, J., and Z. Chen. 2008. Extended Bayesian information criterion for model selection with large model space. Biometrika 94:759–71.
- Efron, B., T. Hastie, I. Johnstone, and R. Tibshirani. 2004. Least angle regression (with discussion). Annals of Statistics 32:407–99.
- Fan, J., and R. Li. 2001. Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Statistical Association 96:1348–60.
- Friedman, J., T. Hastie, H. Hofling, and R. Tibshirani. 2007. Pathwise coordinate optimization. Annals of Applied Statistics 1:302–32.
- Griffin, J., and P. Brown. 2005. Alternative prior distributions for variable selection with very many more variables than observations. Technical Report, University of Warwick, Coventry, UK.
- Griffin, J., and P. Brown. 2011. Bayesian hyper-lassos with non-convex penalization. Australian & New Zealand Journal of Statistics 53:423–42.
- Hoerl. A. E., and R. W. Kennard. 1970. Ridge regression: biased estimation for nonorthogonal problem. Technometrics 12:55–67.
- Hoggart, C. J., J. C. Whittaker, M. D. Iroio, and D. J. Balding. 2008. Simultaneous analysis of all snps in genome-wide and re-sequencing association studies. PLoS Genetics 4:e1000,130.
- Jang, W., J. Lim, M. Lazar, J. Loh, and D. Yu. 2013. Regression shrinkage and grouping of highly correlated predictors with horses. Tech. Rep. arXiv:1302.0256.
- Konishi, S., and G. Kitagawa. 2008. Information criteria and statistical modeling. New York: Springer.
- Konishi, S., T. Ando, and S. Imoto. 2004. Bayesian information criteria and smoothing parameter selection in radial basis function networks. Biometrika 91:27–43.
- Kyung, M., J. Gill, M. Ghosh, and G. Casalla. 2010. Penalized regression, standard error, and Bayesian lasso. Bayesian Analysis 5:369–12.
- Park, T., and G. Casella. 2008. The Bayesian lasso. Journal of the American Statistical Association 103:681–86.
- Rockova, V., and E. Lesaffre. 2014. Incorporating grouping information in bayesian variable selection with applications genomics. Bayesian Analysis 9:221–58.
- Schwarz, G. 1978. Estimating the dimension of a model. Annals of Statistics 6:461–64.
- Shen, X., and H. C. Huang. 2010. Grouping pursuit through a regularization solution surface. Journal of the American Statistical Association 105 (490):727–39.
- Tibshirani, R. 1996. Regression shrinkage and selection via lasso. Journal of the Royal Statistical Society Series B 58:267–88.
- Tibshirani, R., and P. Wang. 2008. Spatial smoothing and hot spot detection for cgh data using the fused lasso. Biostatistics 9:18–29.
- Tibshirani, R., M. Saunders, S. Rosset, J. Zhu, and K. Knight. 2005. Sparsity and smoothness via the fused lasso. Journal of the Royal Statistical Society Series B 67:91–108.