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.
翻译:然而,先前的工作表明,由于偏离模拟假设,其性能可能受到偏离模拟假设的严重影响。由于模拟假设在应用研究中很常见,因此有必要采用某种程度强于误判的推断方法,但同时又简单到可以吸引从业者。我们用直接的半参数随机效果靴子,在模型偏差特性下,为混合效应的群集和同时推论设计了统计工具。我们在理论分析中显示,在一般常态条件下,我们的方法基本一致。在模拟中,我们的间隔对于严重偏离模型假设和在实验覆盖概率方面比竞争者做得好。