Scalable Bayesian Variable Selection Using Nonlocal Prior Densities in Ultrahigh-dimensional Settings.

@article{Shin2018ScalableBV,
  title={Scalable Bayesian Variable Selection Using Nonlocal Prior Densities in Ultrahigh-dimensional Settings.},
  author={Minsuk Shin and Anirban Krishna Bhattacharya and Valen E. Johnson},
  journal={Statistica Sinica},
  year={2018},
  volume={28 2},
  pages={
          1053-1078
        }
}
Bayesian model selection procedures based on nonlocal alternative prior densities are extended to ultrahigh dimensional settings and compared to other variable selection procedures using precision-recall curves. Variable selection procedures included in these comparisons include methods based on g-priors, reciprocal lasso, adaptive lasso, scad, and minimax concave penalty criteria. The use of precision-recall curves eliminates the sensitivity of our conclusions to the choice of tuning… CONTINUE READING
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References

Publications referenced by this paper.
SHOWING 1-10 OF 34 REFERENCES

Bayesian Model Selection in High-Dimensional Settings.

  • Journal of the American Statistical Association
  • 2012
VIEW 8 EXCERPTS
HIGHLY INFLUENTIAL

Consistent high-dimensional Bayesian variable selection via penalized credible regions.

  • Journal of the American Statistical Association
  • 2012
VIEW 5 EXCERPTS
HIGHLY INFLUENTIAL

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