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

Gabriela Ciuperca

Statistics & Probability Letters, 2015, vol. 107, issue C, 313-323

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.

Keywords: High-dimension; Quantile regression; Seamless L0 penalty; Oracle properties; BIC criterion (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1016/j.spl.2015.09.011

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