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- W2906223208 abstract "In spite of its high practical relevance, multiple inference in linear mixed models has hardly been addressed so far. Such inference is interesting for population parameters (marginal) as well as about cluster specific ones (conditional). This work introduces a general framework for multiple inference in linear mixed models for the latter. Consistent simultaneous confidence sets for cluster specific parameters 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. Testing linear hypotheses and multiple comparisons by Tukey's method are considered too. Our inference tools are based on standard quantiles and require no re-sampling techniques. All findings are validated in simulations, and illustrated along a study on Spanish income data." @default.
- W2906223208 created "2019-01-01" @default.
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- W2906223208 date "2018-12-21" @default.
- W2906223208 modified "2023-09-27" @default.
- W2906223208 title "Marginal and Conditional Multiple Inference in Linear Mixed Models" @default.
- W2906223208 cites W1959492 @default.
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- W2906223208 hasPublicationYear "2018" @default.
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