# Uniformly Valid Inference Based on the Lasso in Linear Mixed Models

@inproceedings{Kramlinger2022UniformlyVI, title={Uniformly Valid Inference Based on the Lasso in Linear Mixed Models}, author={Peter Kramlinger and Ulrike Schneider and Tatyana Krivobokova}, year={2022} }

Linear mixed models (LMMs) are suitable for clustered data and are common in e.g. biometrics, medicine, or small area estimation. It is of interest to obtain valid inference after selecting a subset of available variables. We construct conﬁdence sets for the ﬁxed eﬀects in Gaussian LMMs that are estimated via a Lasso-type penalization which allows quantifying the joint uncertainty of both variable selection and estimation. To this end, we exploit the properties of restricted maximum likelihood…

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