TL;DR
The triple shrinkage adaptive GO estimator is proposed, which extends the GO framework with adaptive, coefficient-specific weights, and produces flexible penalization and achieves oracle properties asymptotically, yielding performance comparable to methods that effectively know the true support.
Overall, CDA-LASSO directly incorporates the internal correlation structure of the data into the penalty weights without requiring a predefined graphical structure and provides a practical methodological extension for more controlled and parsimonious variable selection in high-dimensional correlated settings.
Classical canonical correlation analysis becomes numerically unstable when the number of variables is large relative to the sample size and is sensitive to contamination in observations or individual cells. This study develops an integrated robust and regularized procedure that combines bounded cellwise wrapping, shrin...
This work proposes computationally efficient tests for equality of mean vectors of two or more high-dimensional populations by establishing an equivalence between equality of means and a zero population logistic regression parameter.
In real-world applications, data are often error-contaminated; naively applying conventional methods without accommodating the measurement error effects often yields inconsistent estimates. Biased results can be further exacerbated by the ultrahigh-dimensionality of covariates. Focusing on the widely used function-on-s...
We study a class of Lasso based estimators obtained by applying a quadratic correction on the Lasso equicorrelation set. The penalty matrix determines both the magnitude and geometry of the correction and contains, among other cases, the isotropic Lasso--Ridge correction, least squares refitting, Gram proportional inte...
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