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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...

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Main Authors: Muhammad Khan, Dost, Yaqoob, Anum, Iqbal, Nadeem, Abdul Wahid, Khalil, Umair, Khan, Mukhtaj, Abd Rahman, Mohd Amiruddin, Mustafa, Mohd Shafie, Khan, Zardad
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers 2019
Online Access:http://psasir.upm.edu.my/id/eprint/82711/1/Variable%20selection%20.pdf
http://psasir.upm.edu.my/id/eprint/82711/
https://ieeexplore.ieee.org/document/8876588/authors#authors
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spelling my.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
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
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.
format Article
author Muhammad Khan, Dost
Yaqoob, Anum
Iqbal, Nadeem
Abdul Wahid
Khalil, Umair
Khan, Mukhtaj
Abd Rahman, Mohd Amiruddin
Mustafa, Mohd Shafie
Khan, Zardad
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
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
title 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_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_sort variable selection via scad-penalized quantile regression for high-dimensional count data
publisher Institute of Electrical and Electronics Engineers
publishDate 2019
url http://psasir.upm.edu.my/id/eprint/82711/1/Variable%20selection%20.pdf
http://psasir.upm.edu.my/id/eprint/82711/
https://ieeexplore.ieee.org/document/8876588/authors#authors
_version_ 1702171455891963904
score 13.211869

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