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Adaptive sparse group LASSO in quantile regression


This paper studies the introduction of sparse group LASSO (SGL) to the quantile regression framework. Additionally, a more flexible version, an adaptive SGL is proposed based on the adaptive idea, this is, the usage of adaptive weights in the penalization. Adaptive estimators are usually focused on the study of the oracle property under asymptotic and double asymptotic frameworks. A key step on the demonstration of this property is to consider adaptive weights based on a initial \(\sqrt{n}\)-consistent estimator. In practice this implies the usage of a non penalized estimator that limits the adaptive solutions to low dimensional scenarios. In this work, several solutions, based on dimension reduction techniques PCA and PLS, are studied for the calculation of these weights in high dimensional frameworks. The benefits of this proposal are studied both in synthetic and real datasets.

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We appreciate the work of the referees that has contributed to substantially improve the scientific contributions of this work. In this research we have made use of Uranus, a supercomputer cluster located at University Carlos III of Madrid and funded jointly by EU-FEDER funds and by the Spanish Government via the National Projects No. UNC313-4E-2361, No. ENE2009-12213- C03-03, No. ENE2012-33219 and No. ENE2015-68265-P. This research was partially supported by research grants and Project ECO2015-66593-P from Ministerio de Economía, Industria y Competitividad, Project MTM2017-88708-P from Ministerio de Economía y Competitividad, FEDER funds and Project IJCI-2017-34038 from Agencia Estatal de Investigación, Ministerio de Ciencia, Innovación y Universidades.

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Correspondence to Alvaro Mendez-Civieta.

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Mendez-Civieta, A., Aguilera-Morillo, M.C. & Lillo, R.E. Adaptive sparse group LASSO in quantile regression. Adv Data Anal Classif 15, 547–573 (2021).

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  • High-dimension
  • Penalization
  • Regularization
  • Prediction
  • Weight calculation

Mathematics Subject Classification

  • 62J07