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Inference for partial correlation when data are missing not at random

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  • Gorbach, Tetiana
  • de Luna, Xavier

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.

Suggested Citation

  • Gorbach, Tetiana & de Luna, Xavier, 2018. "Inference for partial correlation when data are missing not at random," Statistics & Probability Letters, Elsevier, vol. 141(C), pages 82-89.
    • Handle: RePEc:eee:stapro:v:141:y:2018:i:c:p:82-89
    DOI: 10.1016/j.spl.2018.05.027
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    References listed on IDEAS

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    1. Patrick Puhani, 2000. "The Heckman Correction for Sample Selection and Its Critique," Journal of Economic Surveys, Wiley Blackwell, vol. 14(1), pages 53-68, February.
    2. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    3. Francis Vella, 1998. "Estimating Models with Sample Selection Bias: A Survey," Journal of Human Resources, University of Wisconsin Press, vol. 33(1), pages 127-169.
    4. Minna Genbäck & Elena Stanghellini & Xavier Luna, 2015. "Uncertainty intervals for regression parameters with non-ignorable missingness in the outcome," Statistical Papers, Springer, vol. 56(3), pages 829-847, August.
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