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Strong rules for discarding predictors in lasso‐type problems

Author

Listed:
  • Robert Tibshirani
  • Jacob Bien
  • Jerome Friedman
  • Trevor Hastie
  • Noah Simon
  • Jonathan Taylor
  • Ryan J. Tibshirani

Abstract

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Suggested Citation

  • Robert Tibshirani & Jacob Bien & Jerome Friedman & Trevor Hastie & Noah Simon & Jonathan Taylor & Ryan J. Tibshirani, 2012. "Strong rules for discarding predictors in lasso‐type problems," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 74(2), pages 245-266, March.
  • Handle: RePEc:bla:jorssb:v:74:y:2012:i:2:p:245-266
    DOI: j.1467-9868.2011.01004.x
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    File URL: http://hdl.handle.net/10.1111/j.1467-9868.2011.01004.x
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    Citations

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    Cited by:

    1. Barbaglia, Luca & Wilms, Ines & Croux, Christophe, 2016. "Commodity dynamics: A sparse multi-class approach," Energy Economics, Elsevier, vol. 60(C), pages 62-72.
    2. Gross, Samuel M. & Tibshirani, Robert, 2016. "Data Shared Lasso: A novel tool to discover uplift," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 226-235.
    3. Michoel, Tom, 2016. "Natural coordinate descent algorithm for L1-penalised regression in generalised linear models," Computational Statistics & Data Analysis, Elsevier, vol. 97(C), pages 60-70.
    4. Yen, Tso-Jung & Yen, Yu-Min, 2016. "Structured variable selection via prior-induced hierarchical penalty functions," Computational Statistics & Data Analysis, Elsevier, vol. 96(C), pages 87-103.
    5. Guo, Yi & Berman, Mark & Gao, Junbin, 2014. "Group subset selection for linear regression," Computational Statistics & Data Analysis, Elsevier, vol. 75(C), pages 39-52.
    6. repec:bla:biomet:v:72:y:2016:i:4:p:1086-1097 is not listed on IDEAS

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