论文标题
在模型错误下的线性混合效果的简单引导程序
Simple bootstrap for linear mixed effects under model misspecification
论文作者
论文摘要
线性混合效应被认为是各个域中集群级参数的出色预测指标。但是，以前的工作表明，他们的性能可能会受到与建模假设的不同影响。由于后者在应用研究中很常见，因此需要推论方法，在某种程度上，这在某种程度上对误解了，但同时却足够简单，可以吸引从业者。我们使用直接的半参数随机效应引导程序构建用于集群和同时推断混合效应的统计工具，以同时推断混合效应。在我们的理论分析中，我们表明我们的方法在一般规律条件下渐近一致。在模拟中，我们的时间间隔与模型假设的严重不同，并且在经验覆盖范围方面的表现要比其竞争对手更好。
Linear mixed effects are considered excellent predictors of cluster-level parameters in various domains. However, previous work has shown that their performance can be seriously affected by departures from modelling assumptions. Since the latter are common in applied studies, there is a need for inferential methods which are to certain extent robust to misspecfications, but at the same time simple enough to be appealing for practitioners. We construct statistical tools for cluster-wise and simultaneous inference for mixed effects under model misspecification using straightforward semiparametric random effect bootstrap. In our theoretical analysis, we show that our methods are asymptotically consistent under general regularity conditions. In simulations our intervals were robust to severe departures from model assumptions and performed better than their competitors in terms of empirical coverage probability.
