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Slope heuristics and V-Fold model selection in heteroscedastic regression using strongly localized bases

Author

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  • Fabien Navarro

    (CREST;ENSAI)

  • Adrien Saumard

    (CREST;ENSAI)

Abstract

We investigate the optimality for model selection of the so-called slope heuristics, V -fold cross-validation and V -fold penalization in a heteroscedatic with random design regression context. We consider a new class of linear models that we call strongly localized bases and that generalize histograms, piecewise polynomials and compactly supported wavelets. We derive sharp oracle inequalities that prove the asymptotic optimality of the slope heuristics—when the optimal penalty shape is known—and V -fold penalization. Furthermore, V -fold cross-validation seems to be suboptimal for a ?xed value of V since it recovers asymptotically the oracle learned from a sample size equal to 1-V -1 of the original amount of data. Our results are based on genuine concentration inequalities for the true and empirical excess risks that are of independent interest. We show in our experiments the good behavior of the slope heuristics for the selection of linear wavelet models. Furthermore, V -fold cross-validation and V -fold penalization have comparable e?ciency.

Suggested Citation

  • Fabien Navarro & Adrien Saumard, 2017. "Slope heuristics and V-Fold model selection in heteroscedastic regression using strongly localized bases," Working Papers 2017-67, Center for Research in Economics and Statistics.
    • Handle: RePEc:crs:wpaper:2017-67
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    References listed on IDEAS

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    1. Antoniadis, Anestis & Bigot, Jeremie & Sapatinas, Theofanis, 2001. "Wavelet Estimators in Nonparametric Regression: A Comparative Simulation Study," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 6(i06).
    2. Cai, T. Tony & Brown, Lawrence D., 1999. "Wavelet estimation for samples with random uniform design," Statistics & Probability Letters, Elsevier, vol. 42(3), pages 313-321, April.
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    Cited by:

    1. Christophe Chesneau & Salima El Kolei & Junke Kou & Fabien Navarro, 2019. "Nonparametric estimation in a regression model with additive and multiplicative noise," Papers 1906.07695, arXiv.org, revised Jun 2020.
    2. Fabien Navarro & Adrien Saumard, 2017. "E?ciency of the V-fold model selection for localized bases," Working Papers 2017-65, Center for Research in Economics and Statistics.

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