Review Articles

Semiparametric propensity weighting for nonignorable nonresponse: a discussion of ‘Statistical inference for nonignorable missing data problems: a selective review’ by Niansheng Tang and Yuanyuan Ju

Jun Shao

School of Statistics, East China Normal University, Shanghai, People's Republic of China; Department of Statistics, University of Wisconsin-Madison, Madison, WI, USA

Pages 141-142 | Received 09 Sep. 2018, Accepted 10 Sep. 2018, Published online: 24 Sep. 2018,
  • Abstract
  • Full Article
  • References
  • Citations


  1. Kim, J. K., & Yu, C. L. (2011). A semiparametric estimation of mean functionals with nonignorable missing data. Journal of the American Statistical Association106, 157–165. doi: 10.1198/jasa.2011.tm10104 [Taylor & Francis Online], [Google Scholar]
  2. Lee, S. Y., & Tang, N. S. (2006). Bayesian analysis of nonlinear structural equation models with nonignorable missing data. Psychometrika71, 541–564. doi: 10.1007/s11336-006-1177-1 [Google Scholar]
  3. Qin, J., Leung, D., & Shao, J. (2002). Estimation with survey data under non-ignorable nonresponse or informative sampling. Journal of American Statistical Association97, 93–200. doi: 10.1198/016214502753479338 [Taylor & Francis Online], [Google Scholar]
  4. Shao, J., & Wang, L. (2016). Semiparametric inverse propensity weighting for nonignorable missing data. Biometrika103, 175–187. doi: 10.1093/biomet/asv071 [Google Scholar]
  5. Wang, S., Shao, J., & Kim, J. (2014). An instrumental variable approach for identification and estimation with nonignorable nonresponse. Statistica Sinica24, 1097–1116. [Google Scholar]
  6. Zhao, J., & Shao, J. (2015). Semiparametric pseudo likelihoods in generalized linear models with nonignorable missing data. Journal of American Statistical Association110, 1577–1590. doi: 10.1080/01621459.2014.983234 [Taylor & Francis Online], [Google Scholar]
  7. Zhao, J., & Shao, J. (2017). Approximate conditional likelihood for generalized linear models with general missing data mechanism. Journal of System Science and Complexity30, 139–153. doi: 10.1007/s11424-017-6188-3 [Google Scholar]