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Model selection in high-dimensional quantile regression with seamless L0 penalty

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  • Ciuperca, Gabriela

Abstract

We introduce and study the seamless L0 quantile estimator in a linear model when the number of parameters increases with sample size. For this estimator we derive the convergence rate and oracle properties. A consistent BIC criterion to select the tuning parameters is given.

Suggested Citation

  • Ciuperca, Gabriela, 2015. "Model selection in high-dimensional quantile regression with seamless L0 penalty," Statistics & Probability Letters, Elsevier, vol. 107(C), pages 313-323.
    • Handle: RePEc:eee:stapro:v:107:y:2015:i:c:p:313-323
    DOI: 10.1016/j.spl.2015.09.011
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    References listed on IDEAS

    as
    1. Eun Ryung Lee & Hohsuk Noh & Byeong U. Park, 2014. "Model Selection via Bayesian Information Criterion for Quantile Regression Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 216-229, March.
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    Cited by:

    1. Gabriela Ciuperca, 2019. "Adaptive group LASSO selection in quantile models," Statistical Papers, Springer, vol. 60(1), pages 173-197, February.

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    More about this item

    Keywords

    High-dimension; Quantile regression; Seamless L0 penalty; Oracle properties; BIC criterion;
    All these keywords.

    JEL classification:

    • L0 - Industrial Organization - - General

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