probe

Description

Implements an efficient and powerful Bayesian approach for sparse high-dimensional linear regression. It uses minimal prior assumptions on the parameters through plug-in empirical Bayes estimates of hyperparameters. An efficient Parameter-Expanded Expectation-Conditional-Maximization (PX-ECM) algorithm estimates maximum a posteriori (MAP) values of regression parameters and variable selection probabilities. The PX-ECM results in a robust computationally efficient coordinate-wise optimization, which adjusts for the impact of other predictor variables. The E-step is motivated by the popular two-group approach to multiple testing. The result is a PaRtitiOned empirical Bayes Ecm (PROBE) algorithm applied to sparse high-dimensional linear regression, implemented using one-at-a-time or all-at-once type optimization. More information can be found in McLain, Zgodic, and Bondell (2022) <arXiv:2209.08139>.

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Maintainer: ‘Alexander McLain <mclaina@mailbox.sc.edu>’

The Description field contains
  Zgodic, and Bondell (2022) <arXiv:2209.08139>.
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

Maintainer: ‘Alexander McLain <mclaina@mailbox.sc.edu>’

The Description field contains
  Zgodic, and Bondell (2022) <arXiv:2209.08139>.
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

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Maintainer: ‘Alexander McLain <mclaina@mailbox.sc.edu>’

The Description field contains
  Zgodic, and Bondell (2022) <arXiv:2209.08139>.
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

Maintainer: ‘Alexander McLain <mclaina@mailbox.sc.edu>’

The Description field contains
  Zgodic, and Bondell (2022) <arXiv:2209.08139>.
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

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