Inference for High-Dimensional Linear Mixed-Effects Models: A Quasi-Likelihood Approach

@article{Li2019InferenceFH,
  title={Inference for High-Dimensional Linear Mixed-Effects Models: A Quasi-Likelihood Approach},
  author={Sai Li and T. Tony Cai and Hongzhe Li},
  journal={arXiv: Methodology},
  year={2019}
}
Linear mixed-effects models are widely used in analyzing clustered or repeated measures data. We propose a quasi-likelihood approach for estimation and inference of the unknown parameters in linear mixed-effects models with high-dimensional fixed effects. The proposed method is applicable to general settings where the cluster sizes are possibly large or unbalanced. Regarding the fixed effects, we provide rate optimal estimators and valid inference procedures that are free of the assumptions on… Expand

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