Data-driven Calibration of Penalties for Least-Squares Regression

@article{Arlot2009DatadrivenCO,
  title={Data-driven Calibration of Penalties for Least-Squares Regression},
  author={Sylvain Arlot and Pascal Massart},
  journal={Journal of Machine Learning Research},
  year={2009},
  volume={10},
  pages={245-279}
}
Penalization procedures often suffer from their dependence on multiplying factors, whose optimal values are either unknown or hard to estimate from data. We propose a completely data-driven calibration algorithm for these parameters in the least-squares regression framework, without assuming a particular shape for the penalty. Our algorithm relies on the concept of minimal penalty, recently introduced by Birgé and Massart (2007) in the context of penalized least squares for Gaussian… CONTINUE READING
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Showing 1-10 of 52 references

An optimal selection of regression variables

  • Ritei Shibata
  • Biometrika
  • 1981
Highly Influential
16 Excerpts

Asymptotic optimality for Cp, CL, cross-validation and generalized crossvalidation: discrete index

  • Ker-Chau Li
  • set. Ann. Statist.,
  • 1987
Highly Influential
4 Excerpts

A poor man’s wilks phenomenon

  • Stéphane Boucheron, Pascal Massart
  • Personal communication,
  • 2008
2 Excerpts

Fanny Villers

  • XI Paris, December
  • Tests et sélection de modèles pour l’analyse de…
  • 2008

Polyak and Alexandre B . Tsybakov . Asymptotic optimality of the C ptest in the projection estimation of a regression

  • T. Boris
  • Teor . Veroyatnost . i Primenen .
  • 2008

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