arXiv:2405. 03063v3 Announce Type: replace-cross Abstract: We propose a generalized debiased Lasso estimator based on a stability principle.
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The paper discusses the limitations of LASSO in high‑dimensional regression models when cointegrated regressors are present, noting that the convex penalty can overly shrink their coefficients and lead to inconsistent estimation. It proposes using folded concave penalties (FCP), such as SCAD and MCP, to overcome this issue, establishing both local and global oracle properties for the penalized regression. Simulation results and an application to euro area unemployment forecasting demonstrate that FCP improves variable selection and out‑of‑sample prediction compared to LASSO.
arXiv:2510. 11546v3 Announce Type: replace-cross Abstract: High-dimensional regression often suffers from heavy-tailed noise and outliers, which can severely undermine the reliability of least-squares based methods.
arXiv:2610.08345v1 Announce Type: cross Abstract: Using a replica-symmetric high-dimensional characterization, we develop an inferential framework for sparse support vector machines when the sample s...
arXiv:2610. 29425v1 Announce Type: new Abstract: Understanding generalization of randomized learning algorithms remains a central challenge.
arXiv:2607. 03839v1 Announce Type: new Abstract: Sparse feature selection is critical for high-dimensional machine learning, yet traditional $\ell_1$-regularized methods are often brittle under observational noise and spurious correlations, leading to unstable feature supports and degraded generalization.
arXiv:2606. 11738v1 Announce Type: cross Abstract: We study online estimation for high-dimensional generalized linear models with streaming data.