论文标题
高维广义线性模型的无调山脊估计器
Tuning-free ridge estimators for high-dimensional generalized linear models
论文作者
论文摘要
脊估计器正规化平方的欧几里得参数长度。此类估计器在数学上和计算上具有吸引力，但涉及可能难以校准的调整参数。在本文中，我们表明可以修改脊估计器，以便可以完全避免调谐参数。我们还表明，这些修改的版本可以改善标准脊估计器与交叉验证的经验预测精度，并且我们提供了首先提供理论保证。
Ridge estimators regularize the squared Euclidean lengths of parameters. Such estimators are mathematically and computationally attractive but involve tuning parameters that can be difficult to calibrate. In this paper, we show that ridge estimators can be modified such that tuning parameters can be avoided altogether. We also show that these modified versions can improve on the empirical prediction accuracies of standard ridge estimators combined with cross-validation, and we provide first theoretical guarantees.
