Quantile regression: a penalization approach

María del Carmen Aguilera Morillo

DES - Working Papers. Statistics and Econometrics. WS from Universidad Carlos III de Madrid. Departamento de Estadística

Abstract: Sparse group LASSO (SGL) is a penalization technique used in regression problems where the covariates have a natural grouped structure and provides solutions that are both between and within group sparse. In this paper the SGL is introduced to the quantile regression (QR) framework, and a more flexible version, the adaptive sparse group LASSO (ASGL), is proposed. This proposal adds weights to the penalization improving prediction accuracy. Usually, adaptive weights are taken as a function of the original non-penalized solution model. This approach is only feasible in the n > p framework. In this work, a solution that allows using adaptive weights in high-dimensional scenarios is proposed. The benefits of this proposal are studied both in synthetic and real datasets.

Keywords: Quantile; Regression; Group; Variable; Selection; Adaptive; Sparse; Group; Lasso; High; Dimension; Weight; Calculation (search for similar items in EconPapers)
Date: 2019-05-28
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Persistent link: https://EconPapers.repec.org/RePEc:cte:wsrepe:28428

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