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Bayesian semiparametric modeling of response mechanism for nonignorable missing data


Statistical inference with nonresponse is quite challenging, especially when the response mechanism is nonignorable. In this case, the validity of statistical inference depends on untestable correct specification of the response model. To avoid the misspecification, we propose semiparametric Bayesian estimation in which an outcome model is parametric, but the response model is semiparametric in that we do not assume any parametric form for the nonresponse variable. We adopt penalized spline methods to estimate the unknown function. We also consider a fully nonparametric approach to modeling the response mechanism by using radial basis function methods. Using Pólya–gamma data augmentation, we developed an efficient posterior computation algorithm via Gibbs sampling in which most full conditional distributions can be obtained in familiar forms. The performance of the proposed method is demonstrated in simulation studies and an application to longitudinal data.

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This work is supported by the Japan Society for the Promotion of Science (KAKENHI) grant numbers 18K12757 and 19K14592.

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Correspondence to Shonosuke Sugasawa.

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Sugasawa, S., Morikawa, K. & Takahata, K. Bayesian semiparametric modeling of response mechanism for nonignorable missing data. TEST 31, 101–117 (2022).

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  • Longitudinal data
  • Markov Chain Monte Carlo
  • Multiple imputation
  • Polya-gamma distribution
  • Penalized spline

Mathematics Subject Classification

  • 62D10
  • 62G08
  • 62F15