论文标题
贝叶斯“三明治”以进行方差估算
A Bayesian 'sandwich' for variance estimation
论文作者
论文摘要
对于许多常见的方法，存在大样本的贝叶斯类似物，但对于广泛使用的“三明治”或“鲁棒”方差估计值众所周知。我们审查了三明治方差估算的贝叶斯类似物的现有方法，并提出了一个新的模拟，因为贝叶斯在平衡损耗函数的形式下，将标准参数推断的元素与数据的保真度结合在一起。我们的发展是一般的，对于任何具有独立结果的回归设置，我们都是一般的。作为其频繁主义者的大样本相当于，我们通过仿真显示，即使在模型错误指定下，贝叶斯强大的标准误差估计也可以忠实地量化参数估计的可变性 - 从而保留了原始频繁派版本的主要吸引力。我们在研究NHANES年龄与收缩压之间的关联时，证明了标准误差估计的贝叶斯类似物。
Large-sample Bayesian analogs exist for many frequentist methods, but are less well-known for the widely-used 'sandwich' or 'robust' variance estimates. We review existing approaches to Bayesian analogs of sandwich variance estimates and propose a new analog, as the Bayes rule under a form of balanced loss function, that combines elements of standard parametric inference with fidelity of the data to the model. Our development is general, for essentially any regression setting with independent outcomes. Being the large-sample equivalent of its frequentist counterpart, we show by simulation that Bayesian robust standard error estimates can faithfully quantify the variability of parameter estimates even under model misspecification -- thus retaining the major attraction of the original frequentist version. We demonstrate our Bayesian analog of standard error estimates when studying the association between age and systolic blood pressure in NHANES.
