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

Niansheng Tang and Yuanyuan Ju

Statistical Theory and Related Fields, 2018, vol. 2, issue 2, 105-133

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.

Date: 2018
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DOI: 10.1080/24754269.2018.1522481

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