Multivariate random forest permutation importance is given a conditional sure-screening guarantee and applied to facial-shape GWAS after LASSO pre-screening.
Exploring elastic net and multivariate regression
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abstract
When it comes to datasets with a tremendous amount of predictors, variable reduction techniques such as PCA or FA are often used. In this paper, the elastic net, which lies in between the LASSO method and ridge regression, is used as a variable reduction technique followed by further analysis with multivariate regression. Specifically, a messy only dataset is used to show how it can be 'tidied' up and broken down into sensible subsets using the aforementioned method.
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Theoretical Properties of Multivariate Random Forest in Feature Selection and its Application to Facial Morphology-Gene Detection
Multivariate random forest permutation importance is given a conditional sure-screening guarantee and applied to facial-shape GWAS after LASSO pre-screening.