Debiased lasso after sample splitting for estimation and inference in high-dimensional generalized linear models

We consider random sample splitting for estimation and inference in high-dimensional generalized linear models (GLMs), where we first apply the lasso to select a submodel using one subsample and then apply the debiased lasso to fit the selected model using the remaining subsample. We show that a sample splitting procedure based on the debiased lasso yields asymptotically normal estimates under mild conditions and that multiple splitting can address the loss of efficiency. Our simulation results indicate that using the debiased lasso instead of the standard maximum likelihood method in the estimation stage can vastly reduce the bias and variance of the resulting estimates. Furthermore, our multiple splitting debiased lasso method has better numerical performance than some existing methods for high-dimensional GLMs proposed in the recent literature. We illustrate the proposed multiple splitting method with an analysis of the smoking data of the Mid-South Tobacco Case-Control Study.