Group Identification and Variable Selection in Quantile Regression by Ali Alkenani, Basim Shlaibah Msallam

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Practically effective adjustment variable selection in causal inference by Atsushi Noda, Takashi Isozaki

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Variable Selection of High-Dimensional Spatial Autoregressive Panel Models with Fixed Effects by Miaojie Xia, Yuqi Zhang, Ruiqin Tian

“…Some Monte-Carlo experiments and a real data analysis are conducted to examine the finite sample performance of the proposed variable selection procedure, showing that the proposed variable selection method works satisfactorily.…”
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Variables Selection from the Patterns of the Features Applied to Spectroscopic Data—An Application Case by José L. Romero-Béjar, Francisco Javier Esquivel, José Antonio Esquivel

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A machine learning based variable selection algorithm for binary classification of perinatal mortality by Maryam Sadiq, Ramla Shah

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A machine learning based variable selection algorithm for binary classification of perinatal mortality. by Maryam Sadiq, Ramla Shah

“…A machine learning-based variable selection technique termed as CARS-Logistic model is proposed by coupling competitive adaptive re-weighted sampling(CARS) and logistic regression for binary classification. …”
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A Two-Stage Regularization Method for Variable Selection and Forecasting in High-Order Interaction Model by Yao Dong, He Jiang

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Hybrid Model Based on Genetic Algorithms and SVM Applied to Variable Selection within Fruit Juice Classification by C. Fernandez-Lozano, C. Canto, M. Gestal, J. M. Andrade-Garda, J. R. Rabuñal, J. Dorado, A. Pazos

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Comparative Analysis of Carbon Density Simulation Methods in Grassland Ecosystems: A Case Study from Gansu Province, China by Luyao Wu, Jiaqiang Du, Xinying Liu, Lijuan Li, Xiaoqian Zhu, Xiya Chen, Yue Gong, Yushuo Li

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A Progressive Combined Variable Selection Method for Near-Infrared Spectral Analysis Based on Three-Step Hybrid Strategy by Hongmin Sun, Fanze Kong, Cheng Xiu, Weizheng Shen, Yan Wang

“…A specific variable selection method was proposed based on a three-step hybrid strategy for near-infrared spectral analysis. …”
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Predicted Mean Vote of Subway Car Environment Based on Machine Learning by Kangkang Huang, Shihua Lu, Xinjun Li, Ke Feng, Weiwei Chen, Yi Xia

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Improving wheat yield prediction through variable selection using Support Vector Regression, Random Forest, and Extreme Gradient Boosting by Juan Carlos Moreno Sánchez, Héctor Gabriel Acosta Mesa, Adrián Trueba Espinosa, Sergio Ruiz Castilla, Farid García Lamont

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The Effect of Exposure to Mobile Phones on Electrical Cardiac Measurements: A Multivariate Analysis and a Variable Selection Algorithm to Detect the Relationship With Mean Changes by Nader Alharbi, Mohammed Alassiri

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Classification of short-term flood events using stochastic variable selection and Gaussian Naïve Bayes classifier: A case study of Sirajganj district, Bangladesh by Chandan Mondal, Md Jahir Uddin

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Unbiased Isotonic Regression Tree for Discovering Hidden Heterogeneity in Monotonicity Constraints by Doowon Choi

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Prediction of Quantitative Traits Using Common Genetic Variants: Application to Body Mass Index by Sunghwan Bae, Sungkyoung Choi, Sung Min Kim, Taesung Park

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High-Dimensional Cox Regression Analysis in Genetic Studies with Censored Survival Outcomes by Jinfeng Xu

“…For high-dimensional variable selection in the Cox model with parametric relative risk, we consider the univariate shrinkage method (US) using the lasso penalty and the penalized partial likelihood method using the folded penalties (PPL). …”
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Performance research of the T′ algorithm over GF（2） by GUO Wen-ping 1, AN Jin-liang2

“…The XSL algorithm is a method for solving systems of multivariate polynomial equations based on the linearization method on GF（2）,and the T ’ method is the final stage of the XSL algorithm before linearization.Through analysis revealed that the T ’ algorithm can not achieve its desired end condition Free = T or Free=T-1.In orde to solve the problem,a real end condition and two variable selection principle was proposed for T′ algorithm.Based on probabilistic algorithms and variable values greater probability estimation method to improve performance of the original T ’algorithm.The results show that the improved T ’algorithm can simplify the equations.…”
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Development and validation of a nomogram for predicting perioperative transfusion in children undergoing cardiac surgery with CPB by Wenting Wang, He Wang, Jia Liu, Yu Jin, Bingyang Ji, Jinping Liu

“…Methods From September 2014 to December 2021, 23,884 pediatric patients under the age of 14 were randomly divided into training and testing cohorts at a 7:3 ratio. Variable selection was performed using univariate logistic regression and least absolute shrinkage and selection operator (LASSO) regression. …”
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Stochastic step-wise feature selection for Exponential Random Graph Models (ERGMs). by Helal El-Zaatari, Fei Yu, Michael R Kosorok

“…This study introduces a novel methodology for endogenous variable selection in Exponential Random Graph Models (ERGMs) to enhance the analysis of social networks across various scientific disciplines. …”
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