Bayesian l0-regularized least squares

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...

Full description

Bibliographic Details
Main Authors: Polson, N.G (Author), Sun, L. (Author)
Format: Article
Language:English
Published: John Wiley and Sons Ltd 2019
Subjects:
Bayesian variable selection
Computational efficiency
l0 regularization
L0- regularizations
proximal updating
Sampling
single best replacement
sparsity
spike-and-slab prior
Online Access:View Fulltext in Publisher

Internet

View Fulltext in Publisher

Similar Items