|
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 each of the bootstrap samples for 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 covariates' importance measures. The final set of covariates and their coefficients are determined by averaging bootstrap results obtained from Step 2. The proposed method tends to remove highly correlated variables altogether or select them all, and maintains maximal flexibility in estimating their coefficients, particularly with different signs.
|
