Variable selection via SCAD-penalized quantile regression for high-dimensional count data
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 d...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Institute of Electrical and Electronics Engineers
2019
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| Online Access: | http://psasir.upm.edu.my/id/eprint/82711/1/Variable%20selection%20.pdf |
| _version_ | 1796981068805439488 |
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| author | Muhammad Khan, Dost Yaqoob, Anum Iqbal, Nadeem Abdul Wahid Khalil, Umair Khan, Mukhtaj Abd Rahman, Mohd Amiruddin Mustafa, Mohd Shafie Khan, Zardad |
| author_facet | Muhammad Khan, Dost Yaqoob, Anum Iqbal, Nadeem Abdul Wahid Khalil, Umair Khan, Mukhtaj Abd Rahman, Mohd Amiruddin Mustafa, Mohd Shafie Khan, Zardad |
| author_sort | Muhammad Khan, Dost |
| collection | UPM |
| description | 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. |
| first_indexed | 2024-03-06T10:32:20Z |
| format | Article |
| id | upm.eprints-82711 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T10:32:20Z |
| publishDate | 2019 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | upm.eprints-827112021-06-03T10:41:09Z http://psasir.upm.edu.my/id/eprint/82711/ Variable selection via SCAD-penalized quantile regression for high-dimensional count data Muhammad Khan, Dost Yaqoob, Anum Iqbal, Nadeem Abdul Wahid Khalil, Umair Khan, Mukhtaj Abd Rahman, Mohd Amiruddin Mustafa, Mohd Shafie Khan, Zardad 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. Institute of Electrical and Electronics Engineers 2019 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/82711/1/Variable%20selection%20.pdf 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 https://ieeexplore.ieee.org/document/8876588/authors#authors 10.1109/ACCESS.2019.2948278 |
| spellingShingle | Muhammad Khan, Dost Yaqoob, Anum Iqbal, Nadeem Abdul Wahid Khalil, Umair Khan, Mukhtaj Abd Rahman, Mohd Amiruddin Mustafa, Mohd Shafie Khan, Zardad Variable selection via SCAD-penalized quantile regression for high-dimensional count data |
| title | Variable selection via SCAD-penalized quantile regression for high-dimensional count data |
| title_full | Variable selection via SCAD-penalized quantile regression for high-dimensional count data |
| title_fullStr | Variable selection via SCAD-penalized quantile regression for high-dimensional count data |
| title_full_unstemmed | Variable selection via SCAD-penalized quantile regression for high-dimensional count data |
| title_short | Variable selection via SCAD-penalized quantile regression for high-dimensional count data |
| title_sort | variable selection via scad penalized quantile regression for high dimensional count data |
| url | http://psasir.upm.edu.my/id/eprint/82711/1/Variable%20selection%20.pdf |
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