Linear hypothesis testing in ultra high dimensional generalized linear mixed models

This paper is concerned with linear hypothesis testing problems in ultra high dimensional generalized linear mixed models where the response and the random effects are distribution-free. The constrained-partial-regularization based penalized quasi-likelihood method is proposed and the corresponding statistical properties are studied. To test linear hypotheses, we propose a partial penalized quasi-likelihood ratio test, a partial penalized quasi-score test, and a partial penalized Wald test. The theoretical properties of these three tests are established under both the null and the alternatives. The finite sample performance of the proposed tests has been shown by the simulation studies, and the forest health data is illustrated by our procedure.