![Variable selection for inferential models with relatively high-dimensional data: Between method heterogeneity and covariate stability as adjuncts to robust selection | Scientific Reports Variable selection for inferential models with relatively high-dimensional data: Between method heterogeneity and covariate stability as adjuncts to robust selection | Scientific Reports](https://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fs41598-020-64829-0/MediaObjects/41598_2020_64829_Fig1_HTML.png)
Variable selection for inferential models with relatively high-dimensional data: Between method heterogeneity and covariate stability as adjuncts to robust selection | Scientific Reports
![A random forest method with feature selection for developing medical prediction models with clustered and longitudinal data - ScienceDirect A random forest method with feature selection for developing medical prediction models with clustered and longitudinal data - ScienceDirect](https://ars.els-cdn.com/content/image/1-s2.0-S1532046421000927-ga1.jpg)
A random forest method with feature selection for developing medical prediction models with clustered and longitudinal data - ScienceDirect
![Frontiers | A Simultaneous Feature Selection and Compositional Association Test for Detecting Sparse Associations in High-Dimensional Metagenomic Data Frontiers | A Simultaneous Feature Selection and Compositional Association Test for Detecting Sparse Associations in High-Dimensional Metagenomic Data](https://www.frontiersin.org/files/Articles/837396/fmicb-13-837396-HTML/image_m/fmicb-13-837396-g001.jpg)