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Nonignorable Missing Data, Single Index Propensity Score and Profile Synthetic Distribution Function

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  • Xuerong Chen
  • Denis Heng-Yan Leung
  • Jing Qin

Abstract

In missing data problems, missing not at random is difficult to handle since the response probability or propensity score is confounded with the outcome data model in the likelihood. Existing works often assume the propensity score is known up to a finite dimensional parameter. We relax this assumption and consider an unspecified single index model for the propensity score. A pseudo-likelihood based on the complete data is constructed by profiling out a synthetic distribution function that involves the unknown propensity score. The pseudo-likelihood gives asymptotically normal estimates. Simulations show the method compares favorably with existing methods.

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  • Xuerong Chen & Denis Heng-Yan Leung & Jing Qin, 2022. "Nonignorable Missing Data, Single Index Propensity Score and Profile Synthetic Distribution Function," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(2), pages 705-717, April.
    • Handle: RePEc:taf:jnlbes:v:40:y:2022:i:2:p:705-717
    DOI: 10.1080/07350015.2020.1860065
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    Cited by:

    1. Pengfei Li & Jing Qin & Yukun Liu, 2023. "Instability of inverse probability weighting methods and a remedy for nonignorable missing data," Biometrics, The International Biometric Society, vol. 79(4), pages 3215-3226, December.

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