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   https://doi.org/10.1080/00401706.2023.2197471  

   Chen, Shuoli; He, Kejun; He, Shiyuan; Ni, Yang; Wong, Raymond_K W (October 2023, Technometrics)
2. Predicting implementation of response to intervention in math using elastic net logistic regression

   https://doi.org/10.3389/fpsyg.2024.1410396  

   Wang, Qi; Hall, Garret J; Zhang, Qian; Comella, Sara (October 2024, Frontiers in Psychology)

   IntroductionThe primary objective of this study was to identify variables that significantly influence the implementation of math Response to Intervention (RTI) at the school level, utilizing the ECLS-K: 2011 dataset. MethodsDue to missing values in the original dataset, a Random Forest algorithm was employed for data imputation, generating a total of 10 imputed datasets. Elastic net logistic regression, combined with nested cross-validation, was applied to each imputed dataset, potentially resulting in 10 models with different variables. Variables for the models derived from the imputed datasets were selected using four methods, leading to four candidate models for final selection. These models were assessed based on their performance of prediction accuracy, culminating in the selection of the final model that outperformed the others. Results and discussionMethod₅₀and Method_coefemerged as the most effective, achieving a balanced accuracy of 0.852. The ultimate model selected relevant variables that effectively predicted RTI. The predictive accuracy of the final model was also demonstrated by the receiver operating characteristic (ROC) plot and the corresponding area under the curve (AUC) value, indicating its ability to accurately forecast math RTI implementation in schools for the following year. 

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4. Fast Source Reconstruction via ADMM with Elastic Net Regularization

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   Heredia-Juesas, Juan; Tirado, Luis; Martinez-Lorenzo, Jose A. (July 2018, 2018 IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting)

   Norm-1 regularized optimization algorithms are commonly used for Compressive Sensing applications. In this paper, an optimization algorithm based on the Alternating Direction Method of Multipliers (ADMM) together with the Elastic Net regularization is presented. This type of regularization is a linear combination of the norm-1 and norm-2 regularizations,allowing a solution between the sparsest and the minimum energy solutions, but still enforcing some sparsivity. The combination of these two regularizations and the distributive capabilities of the ADMM algorithm enables a fast sparse signal recovering with minimum error. 

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5. Network topology inference via elastic net structural equation models

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   Traganitis, Panagiotis A.; Shen, Yanning; Giannakis, Georgios B. (July 2017, Proc. of EUSIPCO)