A group adaptive elastic-net approach for variable selection in high-dimensional linear regression

2017 ◽  
Vol 61 (1) ◽  
pp. 173-188 ◽  
Author(s):  
Jianhua Hu ◽  
Jian Huang ◽  
Feng Qiu
Keyword(s):  
Linear Regression ◽  
Variable Selection ◽  
Elastic Net ◽  
High Dimensional ◽  
Adaptive Elastic Net

scholarly journals An Improved Forward Regression Variable Selection Algorithm for High-Dimensional Linear Regression Models

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 129032-129042
Author(s):  
Yanxi Xie ◽  
Yuewen Li ◽  
Zhijie Xia ◽  
Ruixia Yan
Keyword(s):  
Linear Regression ◽  
Variable Selection ◽  
Regression Models ◽  
High Dimensional ◽  
Linear Regression Models ◽  
Selection Algorithm

Early stopping aggregation in selective variable selection ensembles for high-dimensional linear regression models

2018 ◽  
Vol 153 ◽  
pp. 1-11 ◽  
Author(s):  
Chun-Xia Zhang ◽  
Jiang-She Zhang ◽  
Qing-Yan Yin
Keyword(s):  
Linear Regression ◽  
Variable Selection ◽  
Regression Models ◽  
High Dimensional ◽  
Linear Regression Models ◽  
Early Stopping

scholarly journals Variable Selection in a Log–Linear Birnbaum–Saunders Regression Model for High-Dimensional Survival Data via the Elastic-Net and Stochastic EM

Technometrics ◽  
2016 ◽  
Vol 58 (3) ◽  
pp. 383-392 ◽  
Author(s):  
Yukun Zhang ◽  
Xuewen Lu ◽  
Anthony F. Desmond
Keyword(s):  
Variable Selection ◽  
Regression Model ◽  
Survival Data ◽  
Elastic Net ◽  
High Dimensional ◽  
Log Linear ◽  
Stochastic Em

scholarly journals Variable selection via the adaptive elastic net: mathematics success of the students in Singapore and Turkey

2021 ◽  
Vol 1 (1) ◽  
pp. 41-55
Author(s):  
Kadriye Hilal Topal
Keyword(s):  
Variable Selection ◽  
Communication Technologies ◽  
Elastic Net ◽  
Quality Of Education ◽  
Information Communication Technologies ◽  
Information Communication ◽  
Regression Methods ◽  
Adaptive Elastic Net ◽  
Mathematics Success

The quality of education is crucial for its competitiveness in the developing world. International tests are organized at regular intervals to measure the quality of education and to see the place in the ranking of countries. The surveys on these examinations have provided a large number of variables that can be effective on the scores of the tests, including family, teacher, school and course equipment and information communication technologies, etc. The important question is which variables are relevant for the students' achievement in these tests. We investigated the barriers of mathematics success of Turkish students in the TIMSS exam and compared their status with Singaporean students who took part in at top of the ranking in the exam. For this, we employed the adaptive elastic net which is one of the regularized regression methods to dataset and compared their prediction accuracy according to three different alpha levels [0.1; 0.5; 0.9] to determine the model that has high variable selection ability with optimal prediction. The adaptive elastic net with the alpha level [0.9] was selected as superior to others. As the findings, a technology-oriented education system can help to success of the students in Turkey and the countries having similar experiences in international tests.

scholarly journals A One-Covariate at a Time, Multiple Testing Approach to Variable Selection in High-Dimensional Linear Regression Models

2016 ◽  
Vol 2016 (290) ◽  
Author(s):  
Alexander Chudik ◽  
◽  
George Kapetanios ◽  
M. Hashem Pesaran ◽  
◽  
...  
Keyword(s):  
Linear Regression ◽  
Variable Selection ◽  
Regression Models ◽  
Multiple Testing ◽  
High Dimensional ◽  
Linear Regression Models ◽  
Testing Approach
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