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Sequentially additive nonignorable missing data modelling using auxiliary marginal information

Author

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  • Mauricio Sadinle
  • Jerome P Reiter

Abstract

SummaryWe study a class of missingness mechanisms, referred to as sequentially additive nonignorable, for modelling multivariate data with item nonresponse. These mechanisms explicitly allow the probability of nonresponse for each variable to depend on the value of that variable, thereby representing nonignorable missingness mechanisms. These missing data models are identified by making use of auxiliary information on marginal distributions, such as marginal probabilities for multivariate categorical variables or moments for numeric variables. We prove identification results and illustrate the use of these mechanisms in an application.

Suggested Citation

  • Mauricio Sadinle & Jerome P Reiter, 2019. "Sequentially additive nonignorable missing data modelling using auxiliary marginal information," Biometrika, Biometrika Trust, vol. 106(4), pages 889-911.
    • Handle: RePEc:oup:biomet:v:106:y:2019:i:4:p:889-911.
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    File URL: http://hdl.handle.net/10.1093/biomet/asz054
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    Cited by:

    1. Olanrewaju Akande & Gabriel Madson & D. Sunshine Hillygus & Jerome P. Reiter, 2021. "Leveraging auxiliary information on marginal distributions in nonignorable models for item and unit nonresponse," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(2), pages 643-662, April.
    2. Grigory Franguridi & Lidia Kosenkova, 2024. "Closed-form estimation and inference for panels with attrition and refreshment samples," Papers 2410.11263, arXiv.org.
    3. Majid Mojirsheibani, 2022. "On the maximal deviation of kernel regression estimators with NMAR response variables," Statistical Papers, Springer, vol. 63(5), pages 1677-1705, October.
    4. Mojirsheibani, Majid, 2021. "On classification with nonignorable missing data," Journal of Multivariate Analysis, Elsevier, vol. 184(C).

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