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Interval estimation of population means under unknown but bounded probabilities of sample selection

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  • Peter M. Aronow
  • Donald K. K. Lee

Abstract

Applying concepts from partial identification to the domain of finite population sampling, we propose a method for interval estimation of a population mean when the probabilities of sample selection lie within a posited interval. The interval estimate is derived from sharp bounds on the Hajek (1971) estimator of the population mean. We demonstrate the method's utility for sensitivity analysis by applying it to a sample of needles collected as part of a syringe tracking and testing programme in New Haven, Connecticut. Copyright 2013, Oxford University Press.

Suggested Citation

  • Peter M. Aronow & Donald K. K. Lee, 2013. "Interval estimation of population means under unknown but bounded probabilities of sample selection," Biometrika, Biometrika Trust, vol. 100(1), pages 235-240.
    • Handle: RePEc:oup:biomet:v:100:y:2013:i:1:p:235-240
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    File URL: http://hdl.handle.net/10.1093/biomet/ass064
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    Cited by:

    1. Lihua Lei & Roshni Sahoo & Stefan Wager, 2023. "Policy Learning under Biased Sample Selection," Papers 2304.11735, arXiv.org.
    2. Martínez-Ovando Juan Carlos & Olivares-Guzmán Sergio I. & Roldán-Rodríguez Adriana, 2014. "Predictive Inference on Finite Populations Segmented in Planned and Unplanned Domains," Working Papers 2014-04, Banco de México.
    3. Ashesh Rambachan & Amanda Coston & Edward Kennedy, 2022. "Robust Design and Evaluation of Predictive Algorithms under Unobserved Confounding," Papers 2212.09844, arXiv.org, revised May 2024.
    4. Matthew J Tudball & Rachael A Hughes & Kate Tilling & Jack Bowden & Qingyuan Zhao, 2023. "Sample-constrained partial identification with application to selection bias," Biometrika, Biometrika Trust, vol. 110(2), pages 485-498.

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