Inference for partial correlation when data are missing not at random

Tetiana Gorbach and Xavier de Luna

Statistics & Probability Letters, 2018, vol. 141, issue C, 82-89

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.

Keywords: Nonignorable dropout; Uncertainty region; Change–change analysis; Brain markers; Cognition (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1016/j.spl.2018.05.027

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