Inference for partial correlation when data are missing not at random
arXiv:1710.04569 · doi:10.1016/j.spl.2018.05.027
Abstract
We introduce uncertainty regions to perform inference on partial correlations when data are missing not at random. These uncertainty regions are shown to have a desired asymptotic coverage. Their finite sample performance is illustrated via simulations and real data example.