Bayesian Model Selection in High-Dimensional Settings
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Publication:4916502
DOI10.1080/01621459.2012.682536zbMath1261.62024OpenAlexW1990885553MaRDI QIDQ4916502
Valen E. Johnson, David Rossell
Publication date: 22 April 2013
Published in: Journal of the American Statistical Association (Search for Journal in Brave)
Full work available at URL: http://europepmc.org/articles/pmc3867525
intrinsic Bayes factororacleintrinsic priorDantzig selectornonlocal priorelastic netadaptive LASSOnonnegative garroteg-prior
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Uses Software
Cites Work
- The Adaptive Lasso and Its Oracle Properties
- Bayes and empirical-Bayes multiplicity adjustment in the variable-selection problem
- Consistency of objective Bayes factors as the model dimension grows
- Consistency of Bayesian procedures for variable selection
- Estimating the dimension of a model
- Nonconcave penalized likelihood with a diverging number of parameters.
- Least angle regression. (With discussion)
- The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
- From moments of sum to moments of product
- Mixtures of g Priors for Bayesian Variable Selection
- The Bayesian Lasso
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Regularization and Variable Selection Via the Elastic Net
