A Bayesian Hierarchical Model for Relating Multiple SNPs within Multiple Genes to Disease Risk by Lewei Duan, Duncan C. Thomas

“…The model involves variable selection at the SNP level through latent indicator variables and Bayesian shrinkage at the gene level towards a prior mean vector and covariance matrix that depend on external information. …”
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Implementation of stop smoking support by mental healthcare professionals: cross-sectional analysis of why nothing much happens by Eline Meijer

“…This study took a broad approach to understanding implementation of stop smoking support (SSS) by MHCPs (N = 220 for main analyses), incorporating background characteristics, psychosocial factors, client factors, and organizational/environmental factors. Variable selection was based on previous work and the Consolidated Framework for Implementation Research. …”
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Entrepreneurial determinants of Moroccan business failure: entrepreneurial behaviors and attitudes by Youssef Zizi, Amine Jamali-Alaoui, Badreddine El Goumi

“…Additionally, we enhance our dataset by incorporating entrepreneurial variables from the World Bank entrepreneurship database and OMPIC. Applying variable selection techniques and models selection criteria, such as AIC and BIC, the main results indicate that the model composed of variables related to entrepreneurial behavior and attitudes variables, specifically fear of failure rate, perceived capabilities rate, and perceived opportunities rate, better explains bankruptcy rate. …”
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Promoting Variable Effect Consistency in Mixture Cure Model for Credit Scoring by Chenlu Zheng, Jianping Zhu, Xinyan Fan, Song Chen, Zhiyuan Zhang

“…To accommodate high-dimensional credit data, a group lasso penalty is also imposed for variable selection. Simulation shows that the proposed method has competitive performance compared with alternative methods in terms of estimation and prediction. …”
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Machine Learning and Statistical Test–Based Culvert Condition Impact Factor Analysis by Ce Gao, Zhibin Li, Hazem Elzarka, Hongyan Yan, Peijin Li

“…In addition, such a statistical test-assisted factor identification process offered a way of identifying and enhancing the input variable selection for predictive ML model development.…”
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Online Monitoring of Copper Damascene Electroplating Bath by Voltammetry: Selection of Variables for Multiblock and Hierarchical Chemometric Analysis of Voltammetric Data by Aleksander Jaworski, Hanna Wikiel, Kazimierz Wikiel

“…The chief goal of this paper is to introduce to the community of electroanalytical chemists numerous variable selection methods which are well established in spectroscopy and can be successfully applied to voltammetric data analysis.…”
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A robust transfer learning approach for high-dimensional linear regression to support integration of multi-source gene expression data. by Lulu Pan, Qian Gao, Kecheng Wei, Yongfu Yu, Guoyou Qin, Tong Wang

“…Data integration, Variable selection, T distribution, Expectation maximization algorithm, Genotype-Tissue Expression, Cross validation.…”
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Support Vector Regression Based on Grid-Search Method for Short-Term Wind Power Forecasting by Hong Zhang, Lixing Chen, Yong Qu, Guo Zhao, Zhenwei Guo

“…The two main contributions of this paper are as follows. (1) In data preprocessing, each encountered problem of employed real data such as irrelevant, outliers, missing value, and noisy data has been taken into account, the corresponding reasonable processing has been given, and the input variable selection and order estimation are investigated by Partial least squares technique. (2) STWPF is investigated by multiscale support vector regression (SVR) technique, and the parameters associated with SVR are optimized based on Grid-search method. …”
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Spectral Discrimination of Archaeological Sites Previously Occupied by Farming Communities Using In Situ Hyperspectral Data by Olaotse Lokwalo Thabeng, Elhadi Adam, Stefania Merlo

“…First, we tested whether there is a difference in the concentration of elements between different soil types using analysis of variance while random forest (RF) and forward variable selection (FVS) methods were used to select important soil elements for the classification of the archaeological sites. …”
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Non-linear multi-objective optimization model of production planning based on fuzzy logic and machine learning by Zahra Saeidi Mobarakeh, Hossein Amoozadkhalili

“…This model uses the combination of non-dominant fourth sorting genetic algorithms (NSGA-IV) and variable selection network (VSN) in a hybrid framework and provides an advanced and multi-faceted approach to solving complex multi-objective optimization problems. …”
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The Efficacy of Monetary and Fiscal Policies on Economic Growth: Evidence from Thailand by Pathairat Pastpipatkul, Htwe Ko

“…First, we used Bayesian additive regression trees (BART) and Bayesian variable selection (BASAD) methods to determine macro factors with the highest probabilities influencing growth, in addition to monetary and fiscal policy tools during the studied periods. …”
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Risk Prediction Using Genome-Wide Association Studies on Type 2 Diabetes by Sungkyoung Choi, Sunghwan Bae, Taesung Park

“…We first built a prediction model by combining variable selection and prediction methods for type 2 diabetes using Affymetrix Genome-Wide Human SNP Array 5.0 from the Korean Association Resource project. …”
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Effects of Vehicle Restriction Policies on Urban Travel Demand Change from a Built Environment Perspective by Xiaoyun Cheng, Kun Huang, Lei Qu, Tianbao Zhang, Li Li

“…To solve the multicollinearity among the variables and high-dimensional problem, this study utilizes two different penalization-based regression models, the LASSO (least absolute shrinkage and selection operator) and Elastic Net regression algorithms, to achieve the variable selection and explore the impacts of the built environment on the change of travel demand triggered by the LPR policy. …”
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Nomogram-Based Prediction of the Risk of Diabetic Retinopathy: A Retrospective Study by Ruohui Mo, Rong Shi, Yuhong Hu, Fan Hu

“…By operating R software, firstly, the least absolute shrinkage and selection operator (LASSO) regression analysis was used to optimize variable selection by running cyclic coordinate descent with 10 times K cross-validation. …”
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Influence of immune cells and inflammatory factors on Alzheimer’s disease axis: evidence from mediation Mendelian randomization study by Linzhu, Jianxin Zhang, Wenhui Fan, Chen Su, Zhi Jin

“…Multiple MR methods were employed to minimize bias, and detailed descriptions of instrumental variable selection and statistical methods were provided. …”
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Study on the prediction model of non-suicidal self-injury behavior risk during hospitalization for adolescent inpatients with depression based on medical data by Yanyan Zhang, Huirong Guo, Yali Wang, Junru Wang, Yuming Ren

“…The least absolute shrinkage and selection operator (LASSO) regression with tenfold cross-validation was used for variable selection. Multivariable logistic regression was then applied to build the predictive model. …”
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A generative model for evaluating missing data methods in large epidemiological cohorts by Lav Radosavljević, Stephen M. Smith, Thomas E. Nichols

“…We compare our evaluations based on synthetic data to an exemplar study which includes variable selection on a single real imputed dataset, finding only small differences between the imputation methods though with iterative imputation leading to the most informative selection of variables. …”
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Stochastic processes govern gut bacterial community assembly in a Schistosoma mansoni-transmitting snail, Biomphalaria straminea. by Zhanhong Yuan, Jinni Hong, Jehangir Khan, Jinghuang Lu, Benjamin Sanogo, Zhongdao Wu, Xi Sun, Datao Lin

“…Based on the null model analysis, we also found that stochastic processes (based on dispersal limitation, homogenizing dispersal, and undominated processes) play a larger role than deterministic (based on homogeneous selection and variable selection) in driving the snail gut bacterial community assembly. …”
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Nomogram for predicting mild cognitive impairment in Chinese elder CSVD patients based on Boruta algorithm by Yanzi Huang, Wendie Huang, Xiaoming Ma, Guoyin Zhao, Jingwen Kang, Huajie Li, Jingwei Li, Jingwei Li, Jingwei Li, Jingwei Li, Jingwei Li, Jingwei Li, Shiying Sheng, Fengjuan Qian

“…Subsequently, Boruta algorithm was utilized for variable selection based on their importance, followed by logistic regression employing backward stepwise regression. …”
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Explainable surrogate modeling for predicting temperature separation performance of the vortex tube by Hyo Beom Heo, Jun Ho Lee, Jeong Won Yoon, Sangseok Yu, Byoung Jae Kim, Seokyeon Im, Seung Hwan Park

“…This study also introduces genetic programming permutation importance (GPPI), a variable selection method designed to prevent model overfitting. …”
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