Sparse high-dimensional linear regression with a partitioned empirical Bayes ECM algorithm. (arXiv:2209.08139v2 [stat.ME] UPDATED)

Bayesian variable selection methods are powerful techniques for fitting and
inferring on sparse high-dimensional linear regression models. However, many
are computationally intensive or require restrictive prior distributions on
model parameters. Likelihood based penalization methods are more
computationally friendly, but resource intensive refitting techniques are
needed for inference. In this paper, we proposed an efficient and powerful
Bayesian approach for sparse high-dimensional linear regression. Minimal prior
assumptions on the parameters are required through the use of plug-in empirical
Bayes estimates of …

algorithm arxiv bayes linear linear regression regression