Variable Selection via SCAD-Penalized Quantile Regression for High-Dimensional Count Data

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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Bibliographic Details
Main Authors: Dost Muhammad Khan, Anum Yaqoob, Nadeem Iqbal, Abdul Wahid, Umair Khalil, Mukhtaj Khan, Mohd Amiruddin Abd Rahman, Mohd Shafie Mustafa, Zardad Khan
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Count data
high dimensional
jittering
quantile regression
variable selection
zeroinflated
Online Access:https://ieeexplore.ieee.org/document/8876588/

Internet

https://ieeexplore.ieee.org/document/8876588/

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