Review Articles

Statistical methods without estimating the missingness mechanism: a discussion of ‘statistical inference for nonignorable missing data problems: a selective review’ by Niansheng Tang and Yuanyuan Ju

Jiwei Zhao

Department of Biostatistics, State University of New York at Buffalo, Buffalo, NY, USA

Pages 143-145 | Received 04 Sep. 2018, Accepted 09 Sep. 2018, Published online: 20 Sep. 2018,
  • Abstract
  • Full Article
  • References
  • Citations


  1. d'Haultfoeuille, X. (2010). A new instrumental method for dealing with endogenous selection. Journal of Econometrics154, 1–15. doi: 10.1016/j.jeconom.2009.06.005 [Google Scholar]
  2. Fang, F., Zhao, J., & Shao, J. (2018). Imputation-based adjusted score equations in generalized linear models with nonignorable missing covariate values. Statistica Sinica28. [Google Scholar]
  3. Kalbfleisch, J. D. (1978). Likelihood methods and nonparametric tests. Journal of the American Statistical Association73, 167–170. doi: 10.1080/01621459.1978.10480021 [Taylor & Francis Online], [Google Scholar]
  4. Kim, J. K., & Shao, J. (2013). Statistical Methods for Handling Incomplete Data. Boca Raton, FL: Chapman & Hall/CRC. [Crossref], [Google Scholar]
  5. Liang, K.-Y., & Qin, J. (2000). Regression analysis under non-standard situations: a pairwise pseudolikelihood approach. Journal of the Royal Statistical Society: Series B (Statistical Methodology)62, 773–786. doi: 10.1111/1467-9868.00263 [Google Scholar]
  6. Little, R. J. (1988). A test of missing completely at random for multivariate data with missing values. Journal of the American Statistical Association83, 1198–1202. doi: 10.1080/01621459.1988.10478722 [Taylor & Francis Online], [Google Scholar]
  7. Little, R. J., & Rubin, D. B. (2002). Statistical analysis with missing data (2nd). Hoboken, NJ: Wiley. [Google Scholar]
  8. Miao, W., & Tchetgen Tchetgen, E. J. (2016). On varieties of doubly robust estimators under missingness not at random with a shadow variable. Biometrika103, 475–482. doi: 10.1093/biomet/asw016 [Google Scholar]
  9. Molenberghs, G., Fitzmaurice, G., Kenward, M. G., Tsiatis, A. A., & Verbeke, G. (2014). Handbook of Missing Data Methodology. Boca Raton, FL: Chapman & Hall/CRC Press. [Google Scholar]
  10. Tang, G., Little, R. J., & Raghunathan, T. E. (2003). Analysis of multivariate missing data with nonignorable nonresponse. Biometrika90, 747–764. doi: 10.1093/biomet/90.4.747 [Google Scholar]
  11. Tsiatis, A. A. (2006). Semiparametric Theory and Missing Data. New York, NY: Springer. [Google Scholar]
  12. Zhao, J., & Ma, Y. (2018). Optimal pseudolikelihood estimation in the analysis of multivariate missing data with nonignorable nonresponse. Biometrika105, 479–486. doi: 10.1093/biomet/asy007 [Google Scholar]
  13. Zhao, J., & Shao, J. (2015). Semiparametric pseudo-likelihoods in generalized linear models with nonignorable missing data. Journal of the American Statistical Association110, 1577–1590. doi: 10.1080/01621459.2014.983234 [Taylor & Francis Online], [Google Scholar]
  14. Zhao, J., & Shao, J. (2017). Approximate conditional likelihood for generalized linear models with general missing data mechanism. Journal of Systems Science and Complexity30, 139–153. doi: 10.1007/s11424-017-6188-3 [Web of Science ®], [Google Scholar]
  15. Zhao, J., & Yang, Y. (2017). Tuning parameter selection in the LASSO with unspecified propensity. In D.-G. Chen, Z. Jin, G. Li, Y. Li, A. Liu, & Y. Zhao (Eds.), New Advances in Statistics and Data Science (pp. 109–125). New York, NY: Springer. [Google Scholar]
  16. Zhao, J., Yang, Y., & Ning, Y. (2018). Penalized pairwise pseudo likelihood for variable selection with nonignorable missing data. Statistica Sinica28. [Google Scholar]

Jiwei Zhao, Yanyuan Ma. (2021) A Versatile Estimation Procedure Without Estimating the Nonignorable Missingness MechanismJournal of the American Statistical Association 0:0, pages 1-15.