Marginal and Conditional Multiple Inference for Linear Mixed Model Predictors

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 cluster specific predictors. Consistent simultaneous confidence sets for cluster specific predictors are constructed. Furthermore, it is shown that, while these simultaneous conditional confidence sets are feasible, remarkably, corresponding marginal confidence sets are also asymptotically valid for conditional inference. Those lend themselves for testing linear hypotheses using standard quantiles without the need of re-sampling techniques. All findings are validated in simulations and illustrated along a study on Spanish income data.