In some heavily parameterized models, one may benefit from shifting some of parameters towards a common target. We consider shrinkage towards an equal parameter value that balances between unrestricted estimation (i.e. allowing full heterogeneity) and estimation under equality restriction (i.e. imposing full homogeneity). The penalty parameter of such ridge regression estimator is tuned using leave-one-out cross-validation. The reduction in predictive mean squared error tends to increase with the dimensionality of the parameter set. We illustrate the benefit of such shrinkage with a few stylized examples. We also work out an example of a heterogeneous panel model, including estimation on real data.

中文翻译：

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线性模型的同质性脊

在一些高度参数化的模型中，将一些参数转移到一个共同的目标可能会受益。我们考虑收缩到一个相等的参数值，在不受限制的估计（即允许完全异质性）和平等限制下的估计（即强加完全同质性）之间取得平衡。这种岭回归估计器的惩罚参数使用留一法交叉验证进行调整。预测均方误差的减少往往随着参数集的维数而增加。我们用一些程式化的例子来说明这种收缩的好处。我们还制定了一个异构面板模型的示例，包括对真实数据的估计。