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...
Saved in:
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 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Penalized LAD-SCAD estimator based on robust wrapped correlation screening method for high dimensional models
by: Baba, Ishaq Abdullahi, et al.
Published: (2021) -
Penalized regression method in high dimensional data
by: Jaafar, Zuharah, et al.
Published: (2022) -
Elastic net penalized Quantile Regression Model and Empirical Mode Decomposition for Improving the Accuracy of the Model Selection
by: Ali S.A. Ambark, et al.
Published: (2023) -
Prediction of heat waves in Pakistan using quantile regression forests
by: Khan, Najeebullah, et al.
Published: (2019) -
The asymmetric effects of oil price on sectoral Islamic stocks: New evidence from quantile-on-quantile regression approach
by: Chang, Bisharat Hussain, et al.
Published: (2020)