@misc{Kramlinger2019Marginal,
 abstract = {This work introduces a general framework for multiple inference in linear mixed models. Such can be done about population parameters (marginal) and subject specific ones (conditional). For two asymptotic scenarios that adequately address settings arising in practice, consistent simultaneous confidence sets for subject specific effects are constructed. In particular, it is shown that while conditional confidence sets are feasible, remarkably, marginal confidence sets are also asymptotically valid for conditional inference. Testing linear hypotheses and multiple comparisons by Tukey's method are also considered. The asymptotic inference is based on standard quantiles and requires no re-sampling techniques. All findings are validated in a simulation study and illustrated by a real data example on Spanish income data.},
 author = {Kramlinger, Peter and Krivobokova, Tatyana and Sperlich, Stefan},
 date = {2018},
 title = {Marginal and Conditional Multiple Inference in Linear Mixed Models},
 url = {https://arxiv.org/abs/1812.09250v2},
 file = {67431424-abfe-4e8e-b6e5-6f4bd5f7cf4b:C\:\\Users\\Barbara\\AppData\\Local\\Swiss Academic Software\\Citavi 6\\ProjectCache\\f4q6lsjyptp8cy52jyikg6ejy8o6t76o6gw6o347n6vucmn\\Citavi Attachments\\67431424-abfe-4e8e-b6e5-6f4bd5f7cf4b.pdf:pdf},
 year = {2018}
}

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