Bayesian l0-regularized least squares
Bayesian l0-regularized least squares is a variable selection technique for high-dimensional predictors. The challenge is optimizing a nonconvex objective function via search over model space consisting of all possible predictor combinations. Spike-and-slab (aka Bernoulli-Gaussian) priors are the go...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
John Wiley and Sons Ltd
2019
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| Subjects: | |
| Online Access: | View Fulltext in Publisher |