Generalized rank regression, which is a class of weighted rank regression with weights based on factor space, provides a powerful tool for conducting robust estimation. In this article, we first establish the asymptotic properties of generalized rank regression under local model misspecification. We then apply the generalized rank regression to the focus information criterion and frequentist model averaging and establish their properties.

中文翻译：

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聚焦信息准则和模型平均与广义秩回归

广义秩回归是一类基于因子空间权重的加权秩回归，为进行稳健估计提供了强大的工具。在本文中，我们首先建立了局部模型错误指定下广义秩回归的渐近性质。然后我们将广义秩回归应用于焦点信息标准和频率模型平均并建立它们的属性。