Robust learning for ridge-penalized quasi-GLMs under non-identical distributions.

ABSTRACT: In recent years, robust estimation of model parameters has attracted considerable attention in statistics and machine learning, particularly in the context of modeling data with outliers and related inverse problems. This paper introduces a novel log-truncated minimization estimator and a corresponding stochastic gradient descent (SGD) algorithm for quasi-generalized linear models. This approach provides a robust alternative to ordinary GLMs without assuming light-tailed error distributions. For independent non-identical distributed (i.n.i.d.) data, we derive non-asymptotic excess risk and [Formula: see text]-risk bounds using log-truncated Lipschitz losses, assuming only a finite β-th moment for [Formula: see text]. Notably, our estimator does not require higher moments or finite variance

SUBMITTER: Zhang H 

PROVIDER: S-EPMC12618890 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature