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Group Identification and Variable Selection in Quantile Regression

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  • Ali Alkenani
  • Basim Shlaibah Msallam

Abstract

Using the Pairwise Absolute Clustering and Sparsity (PACS) penalty, we proposed the regularized quantile regression QR method (QR-PACS). The PACS penalty achieves the elimination of insignificant predictors and the combination of predictors with indistinguishable coefficients (IC), which are the two issues raised in the searching for the true model. QR-PACS extends PACS from mean regression settings to QR settings. The paper shows that QR-PACS can yield promising predictive precision as well as identifying related groups in both simulation and real data.

Suggested Citation

  • Ali Alkenani & Basim Shlaibah Msallam, 2019. "Group Identification and Variable Selection in Quantile Regression," Journal of Probability and Statistics, Hindawi, vol. 2019, pages 1-7, April.
    • Handle: RePEc:hin:jnljps:8504174
    DOI: 10.1155/2019/8504174
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