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

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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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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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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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Robust Bayesian Regularized Estimation Based on t Regression Model by Zean Li, Weihua Zhao

“…In this paper, in view of the advantages of Bayesian analysis, we propose a new robust coefficient estimation and variable selection method based on Bayesian adaptive Lasso t regression. …”
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Modeling Pan Evaporation for Kuwait by Multiple Linear Regression by Jaber Almedeij

“…Multiple linear regression technique is used with a procedure of variable selection for fitting the best model forms. The correlations of evaporation with temperature and relative humidity are also transformed in order to linearize the existing curvilinear patterns of the data by using power and exponential functions, respectively. …”
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Robust Stability Best Subset Selection for Autocorrelated Data Based on Robust Location and Dispersion Estimator by Hassan S. Uraibi, Habshah Midi, Sohel Rana

“…Stability selection (multisplit) approach is a variable selection procedure which relies on multisplit data to overcome the shortcomings that may occur to single-split data. …”
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HighDimMixedModels.jl: Robust high-dimensional mixed-effects models across omics data. by Evan Gorstein, Rosa Aghdam, Claudia Solís-Lemus

“…Our simulations provide new insights into the algorithm's behavior in these settings, and, comparing the performance of two popular penalties, we demonstrate that the smoothly clipped absolute deviation (SCAD) penalty consistently outperforms the least absolute shrinkage and selection operator (LASSO) penalty in terms of both variable selection and estimation accuracy across omics data. …”
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