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Statistical inference with semiparametric nonignorable nonresponse models

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  • Masatoshi Uehara
  • Danhyang Lee
  • Jae‐Kwang Kim

Abstract

How to deal with nonignorable response is often a challenging problem encountered in statistical analysis with missing data. Parametric model assumption for the response mechanism is sensitive to model misspecification. We consider a semiparametric response model that relaxes the parametric model assumption in the response mechanism. Two types of efficient estimators, profile maximum likelihood estimator and profile calibration estimator, are proposed, and their asymptotic properties are investigated. Two extensive simulation studies are used to compare with some existing methods. We present an application of our method using data from the Korean Labor and Income Panel Survey.

Suggested Citation

  • Masatoshi Uehara & Danhyang Lee & Jae‐Kwang Kim, 2023. "Statistical inference with semiparametric nonignorable nonresponse models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 50(4), pages 1795-1817, December.
    • Handle: RePEc:bla:scjsta:v:50:y:2023:i:4:p:1795-1817
    DOI: 10.1111/sjos.12652
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