p-Values for High-Dimensional Regression

@article{Meinshausen2008pValuesFH,
  title={p-Values for High-Dimensional Regression},
  author={N. Meinshausen and Lukas Meier and Peter B{\"u}hlmann},
  journal={Journal of the American Statistical Association},
  year={2008},
  volume={104},
  pages={1671 - 1681}
}
  • N. Meinshausen, Lukas Meier, Peter Bühlmann
  • Published 2008
  • Mathematics
  • Journal of the American Statistical Association
  • Assigning significance in high-dimensional regression is challenging. Most computationally efficient selection algorithms cannot guard against inclusion of noise variables. Asymptotically valid p-values are not available. An exception is a recent proposal by Wasserman and Roeder that splits the data into two parts. The number of variables is then reduced to a manageable size using the first split, while classical variable selection techniques can be applied to the remaining variables, using the… CONTINUE READING
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    References

    SHOWING 1-10 OF 28 REFERENCES
    On Model Selection Consistency of Lasso
    • 2,207
    • PDF
    Bolasso: model consistent Lasso estimation through the bootstrap
    • 352
    • PDF
    Boosting for high-dimensional linear models
    • 136
    • Highly Influential
    • PDF
    Relaxed Lasso
    • 277
    • PDF
    LASSO-TYPE RECOVERY OF SPARSE REPRESENTATIONS FOR HIGH-DIMENSIONAL DATA
    • 736
    • PDF
    BOOSTING FOR HIGH-DIMENSIONAL LINEAR MODELS
    • 229
    Regression Shrinkage and Selection via the Lasso
    • 30,752
    • Highly Influential
    • PDF