TL;DR
The paper introduces a computationally intensive random lasso method for variable selection in linear models, improving upon existing methods by handling correlated variables better and allowing more variables to be selected.
Contribution
It presents a novel two-step bootstrap-based random lasso approach that enhances variable selection and coefficient estimation, especially for microarray data.
Findings
Outperforms traditional lasso and elastic-net in simulations
Selects highly correlated variables more effectively
Achieves competitive or superior prediction accuracy
Abstract
We propose a computationally intensive method, the random lasso method, for variable selection in linear models. The method consists of two major steps. In step 1, the lasso method is applied to many bootstrap samples, each using a set of randomly selected covariates. A measure of importance is yielded from this step for each covariate. In step 2, a similar procedure to the first step is implemented with the exception that for each bootstrap sample, a subset of covariates is randomly selected with unequal selection probabilities determined by the covariates' importance. Adaptive lasso may be used in the second step with weights determined by the importance measures. The final set of covariates and their coefficients are determined by averaging bootstrap results obtained from step 2. The proposed method alleviates some of the limitations of lasso, elastic-net and related methods noted…
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