Authors :
Adamu Buba; Umar Usman; Yakubu Musa; Murtala Muhammed Hamza
Volume/Issue :
Volume 8 - 2023, Issue 9 - September
Google Scholar :
https://tinyurl.com/rykhfsu9
Scribd :
https://tinyurl.com/9udpsvcs
DOI :
https://doi.org/10.5281/zenodo.10029183
Abstract :
Conventional regularization techniques like
LASSO, SCAD and MCP have been shown to perform
poorly in the presence of extremely large or ultra-high
dimensional covariates. This has created the need for
and led to the development and reliance on filtering
technique like screening. Screening techniques (such as
SIS, DC-SIS, and DC – RoSIS) have been shown to
reduce the computational complexity in selecting
important covariates from ultrahigh dimensional
candidates. To this end, there have been various
attempts to hybridize the conventional regularization
techniques. In this paper, we combine some
regularization techniques (LASSO and SCAD) with a
screening technique (DC – RoSIS) to form new hybrid
methods with a view to achieving better dimension
reduction and variable selection simultaneously.
Extensive simulation results and real life data
performance show that the proposed methods perform
better than the conventional methods.
Keywords :
Regularization Techniques, Screening Technique, LASSO DC–RoSIS, SCAD DC – RoSIS.
Conventional regularization techniques like
LASSO, SCAD and MCP have been shown to perform
poorly in the presence of extremely large or ultra-high
dimensional covariates. This has created the need for
and led to the development and reliance on filtering
technique like screening. Screening techniques (such as
SIS, DC-SIS, and DC – RoSIS) have been shown to
reduce the computational complexity in selecting
important covariates from ultrahigh dimensional
candidates. To this end, there have been various
attempts to hybridize the conventional regularization
techniques. In this paper, we combine some
regularization techniques (LASSO and SCAD) with a
screening technique (DC – RoSIS) to form new hybrid
methods with a view to achieving better dimension
reduction and variable selection simultaneously.
Extensive simulation results and real life data
performance show that the proposed methods perform
better than the conventional methods.
Keywords :
Regularization Techniques, Screening Technique, LASSO DC–RoSIS, SCAD DC – RoSIS.
