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Dirichlet-Laplace priors for optimal shrinkage
Penalized regression methods, such as L(1) regularization, are routinely used in high-dimensional applications, and there is a rich literature on optimality properties under sparsity assumptions. In the Bayesian paradigm, sparsity is routinely induced through two-component mixture priors having a pr...
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| I publikationen: | J Am Stat Assoc |
|---|---|
| Huvudupphovsmän: | , , , |
| Materialtyp: | Artigo |
| Språk: | Inglês |
| Publicerad: |
2014
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| Ämnen: | |
| Länkar: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4803119/ https://ncbi.nlm.nih.gov/pubmed/27019543 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1080/01621459.2014.960967 |
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