References
- Albert, J. H., Chib, S. (1993). Bayesian analysis of binary and polychotomous response data. Journal of the American Statistical Association 88:669–679.
- Baragatti, M., Pommeret, D. (2012). A study of variable selection using g-prior distribution with ridge parameter. Computational Statistics & Data Analysis 56:1920–1934.
- Barbieri, M. M., Berger, J. O. (2004). Optimal predictive model selection. Annals of Statistics 32:870–897.
- Bhattacharya, A., Chakraborty, A., Mallick, B. K. (2016). Fast sampling with Gaussian scale-mixture priors in high-dimensional regression. Biometrika 103:985–991.
- Brooks, S. P., Giudici, P., Roberts, G. O. (2003). Efficient construction of reversible jump Markov chain Monte Carlo proposal distributions. Journal of the Royal Statistical Society, Series B 65:3– 55.
- Brown, P. J., Vannucci, M., Fearn, T. (1998). Bayesian wavelength selection in multicomponent analysis. Journal of Chemometrics 12:173–182.
- Chen, Z., Dunson, D. B. (2003). Random effects selection in linear mixed models. Biometrics 59:762–769.
- Díaz-Uriarte, R., Alvarez de Andrés, S. (2006). Gene selection and classification of microarray data using random forest. BMC Bioinformatics 7:3.
- Fridley, B. L. (2009). Bayesian variable and model selection methods for genetic association studies. Genetic Epidemiology 33:27–37.
- Gelman, A. (2005). Prior distributions for variance parameters in hierarchical models. Bayesian Analysis 1:1–19.
- George, E. I., McCulloch, R. E. (1993). Variable selection via Gibbs sampling. Journal of the American Statistical Association 88:881–889.
- George, E. I., McCulloch, R. E. (1997). Approaches for Bayesian variable selection. Statistica Sinica 7:339–373.
- Green, P. J. (1995). Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 82:711–732.
- Gupta, M., Ibrahim, J. (2007). Variable selection in regression mixture modeling for the discovery of gene regulatory networks. Journal of the American Statistical Association 102:867– 880.
- Heidelberger, P., Welch, P. (1983). Simulation run length control in the presence of an initial transient. Operations Research 31:1109–1144.
- Holmes, C. C., Held, L. (2006). Bayesian auxiliary variable models for binary and multinomial regression. Bayesian Analysis 1:145–168.
- Kass, R. E., Carlin, B. P., Gelman, A., Neal, R. M. (1998). Markov chain Monte Carlo in practice: A roundtable discussion. The American Statistician 52:93–100.
- Kinney, S. K., Dunson, D. B. (2007). Fixed and random effects selection in linear and logistic models. Biometrics 63:690–698.
- Kyung, M., Gill, J., Casella, G. (2010). Penalized regression, standard errors, and Bayesian Lassos. Bayesian Analysis 5:369–412.
- Lamnisos, D., Griffin, J. E., Steel, M. F. J. (2009). Transdimensional sampling algorithms for Bayesian variable selection in classification problems with many more variables than observations. Journal of Computational and Graphical Statistics 18:592–612.
- Leamer, E. E. (1978). Specification searches: Ad hoc inference with nonexperimental data. New York: Wiley.
- Lewontin, R. C. (1964). The interaction of selection and linkage. I. General considerations; heterotic models. Genetics 49:49–67.
- Ley, E., Steel, M. F. J. (2009). On the effect of prior assumptions in Bayesian model averaging with applications to growth regression. Journal of Applied Econometrics 24:651–674.
- Li, J., Das, K., Fu, G., Li, R., Wu, R. (2011). The Bayesian Lasso for genome-wide association studies. Bioinformatics 27:516–523.
- Overstall, A. M., Forster, J. J. (2010). Default Bayesian model determination methods for generalized linear mixed models. Computational Statistics & Data Analysis 54:3269–3288.
- Park, T., Casella, G. (2008). The Bayesian Lasso. Journal of the American Statistical Association 103:681–686.
- Polson, N. G., Scott, J. G., Windle, J. (2013). Bayesian inference for logistic models using Pόlya-Gamma latent variables. Journal of the American Statistical Association 108:1339–1349.
- Roberts, G. O., Rosenthal, J. S. (2001). Optimal scaling for various Metropolis-Hastings algorithms. Statistical Science 16:351–367.
- Smith, M., Kohn, R. (1997). Non parametric regression using Bayesian variable selection. Journal of Econometrics 75:317–344.
- Tsai, M. Y. (2015). Variable selection in Bayesian generalized linear mixed models: An illustration using candidate gene case-control association studies. Biometrical Journal 57:234–253.
- West, M. (1987). On scale mixtures of normal distributions. Biometrika 74:646–648.
- Zellner, A., Siow, A. (1980). Posterior odds ratios for selected regression hypotheses. Bayesian Statistics 31:585–603.