<p>Simultaneous estimation and variable selection become difficult in linear models when the design is high-dimensional and predictors are correlated. Although the Lasso and related shrinkage estimators control model complexity, their selection can be inconsistent under certain conditions. To address this, we propose the triple shrinkage adaptive GO estimator, which extends the GO framework with adaptive, coefficient-specific weights. This multi-level shrinkage produces flexible penalization and achieves oracle properties asymptotically, yielding performance comparable to methods that effectively know the true support. The new estimator preserves the grouping effect, a key characteristic of the adaptive ElasticNet, such that coefficients of highly correlated predictors are shrunk toward one another. An efficient algorithm compatible with existing Lasso solutions makes this estimator computationally viable. The proposed approach, therefore, offers a robust alternative for improving estimation accuracy and support recovery in sparse linear models with dependent predictors.</p>