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Statistical inference for nonignorable missing-data problems: a selective review

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  • Niansheng Tang
  • Yuanyuan Ju

Abstract

Nonignorable missing data are frequently encountered in various settings, such as economics, sociology and biomedicine. We review statistical inference for nonignorable missing-data problems, including estimation, influence analysis and model selection. For estimation of mean functionals, we review semiparametric method and empirical likelihood (EL) approach. For estimation of parameters in exponential family nonlinear structural equation models, we introduce expectation-maximisation algorithm, Bayesian approach, and Bayesian EL method. For influence analysis, we investigate the case-deletion method and local influence analysis method from the frequentist and Bayesian viewpoints. For model selection, we present the modified Akaike information criterion and penalised method.

Suggested Citation

  • Niansheng Tang & Yuanyuan Ju, 2018. "Statistical inference for nonignorable missing-data problems: a selective review," Statistical Theory and Related Fields, Taylor & Francis Journals, vol. 2(2), pages 105-133, July.
    • Handle: RePEc:taf:tstfxx:v:2:y:2018:i:2:p:105-133
    DOI: 10.1080/24754269.2018.1522481
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    Cited by:

    1. Maciej Berk{e}sewicz & Dagmara Nikulin, 2019. "Estimation of the size of informal employment based on administrative records with non-ignorable selection mechanism," Papers 1906.10957, arXiv.org.
    2. Guo, Xu & Song, Lianlian & Fang, Yun & Zhu, Lixing, 2019. "Model checking for general linear regression with nonignorable missing response," Computational Statistics & Data Analysis, Elsevier, vol. 138(C), pages 1-12.
    3. Maciej Berȩsewicz & Dagmara Nikulin, 2021. "Estimation of the size of informal employment based on administrative records with non‐ignorable selection mechanism," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(3), pages 667-690, June.

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