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Variable selection via SCAD-penalized quantile regression for high-dimensional count data


Muhammad Khan, Dost and Yaqoob, Anum and Iqbal, Nadeem and Abdul Wahid and Khalil, Umair and Khan, Mukhtaj and Abd Rahman, Mohd Amiruddin and Mustafa, Mohd Shafie and Khan, Zardad (2019) Variable selection via SCAD-penalized quantile regression for high-dimensional count data. IEEE Access, 7. pp. 153205-153216. ISSN 2169-3536


This article introduces a quantile penalized regression technique for variable selection and estimation of conditional quantiles of counts in sparse high-dimensional models. The direct estimation and variable selection of the quantile regression is not feasible due to the discreteness of the count data and non-differentiability of the objective function, therefore, some smoothness must be artificially imposed on the problem. To achieve the necessary smoothness, we use the Jittering process by adding a uniformly distributed noise to the response count variable. The proposed method is compared with the existing penalized regression methods in terms of prediction accuracy and variable selection. We compare the proposed approach in zero-inflated count data regression models and in the presence of outliers. The performance and implementation of the proposed method are illustrated by detailed simulation studies and real data applications.

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Additional Metadata

Item Type: Article
Divisions: Faculty of Science
DOI Number: https://doi.org/10.1109/ACCESS.2019.2948278
Publisher: Institute of Electrical and Electronics Engineers
Keywords: Count data; High dimensional; Jittering; Quantile regression; Variable selection; Zero-inflated
Depositing User: Mr. Sazali Mohamad
Date Deposited: 03 Jun 2021 10:41
Last Modified: 03 Jun 2021 10:41
Altmetrics: http://www.altmetrics.com/details.php?domain=pasir.upm.edu.my&doi=10.1109/ACCESS.2019.2948278
URI: http://psasir.upm.edu.my/id/eprint/82711
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