Elastic Net Regression Modeling With the Orthant Normal Prior

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Dirichlet-Laplace priors for optimal shrinkage (2014, background): “Computational issues and consideration that many of the θjs may be small but not exactly zero has motivated a rich literature on continuous shrinkage priors; for some flavor of the vast literature refer to [3, 7, 15, 17, 25].”
Decoupling Shrinkage and Selection in Bayesian Linear Models: A Posterior Summary Perspective (2014, background): “appear in Liang et al. [2008]. The analytic tractability of the g-prior continues to stimulate current research; see for example Maruyama and George [2011]. A notable recent alternative to g-priors is described in Johnson and Rossell [2012], who introduce priors with density equal to zero at the ori”
Hierarchical Shrinkage Priors for Regression Models (2017, background): “…2008; Hans, 2009) (leading to the Bayesian Lasso), the normal-gamma (Caron and Doucet, 2008; Griffin and Brown, 2010) the Bayesian elastic net (Hans, 2011), the horseshoe prior (Carvalho et al., 2010), the normal-exponential-gamma (NEG) (Griffin and Brown, 2011), the generalized Beta mixtures…”
Tuning Parameter Selection for the Adaptive Lasso Using ERIC (2015, background): “More generally, the principle of formulating an information criterion which accounts for the effect of prior information on the bias-variance tradeoff should, in principle, be applicable to any penalty that has a proper prior distribution, such as the Bayesian elastic net (Hans 2011).”