.png){kind=icon}
返回
Marginal and Conditional Multiple Inference for Linear Mixed Model Predictors
.png){kind=icon}
OA
AI
DOI：10.1080/01621459.2022.2044826.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In spite of its high practical relevance, cluster specific multiple inference for linear mixed model predictors has hardly been addressed so far. While marginal inference for population parameters is well understood, conditional inference for the cluster specific predictors is more intricate. This work introduces a general framework for multiple inference in linear mixed models for duster specific predictors. Consistent confidence sets for multiple inference are constructed under both, the marginal and the conditional law. Furthermore, it is shown that, remarkably, corresponding multiple marginal confidence sets are also asymptotically valid for conditional inference. Those lend themselves for testing linear hypotheses using standard quantiles without the need of resampling techniques. All findings are validated in simulations and illustrated along a study on Covid-19 mortality in the U.S. state prisons. Supplementary materials for this article are available online.
Keyword:
Linear mixed models
Mixed parameters
Multiple testing
Simultaneous inference
Small area estimation
期刊
J
IF:
3
论文数:
5.2K
被引数:
4.8W
机构
引用论文
Tuning the water permeability of ultra-high molecular weight polyethylene microporous membrane by molecular self-assembly and flow field.png)
Polymer
PolymerIF0
PREAI
分享收藏分享收藏
查看更多内容