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Estimation with missing data: beyond double robustness

Citations

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Cited by:

  1. Chen, Sixia & Haziza, David, 2023. "A unified framework of multiply robust estimation approaches for handling incomplete data," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
  2. Shu Yang & Yunshu Zhang, 2023. "Multiply robust matching estimators of average and quantile treatment effects," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 50(1), pages 235-265, March.
  3. Xiong, Wei & Wang, Dehui & Deng, Dianliang & Wang, Xinyang & Zhang, Wanying, 2022. "Penalized multiply robust estimation in high-order autoregressive processes with missing explanatory variables," Journal of Multivariate Analysis, Elsevier, vol. 187(C).
  4. He, Yizeng & Kim, Soyoung & Kim, Mi-Ok & Saber, Wael & Ahn, Kwang Woo, 2021. "Optimal treatment regimes for competing risk data using doubly robust outcome weighted learning with bi-level variable selection," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
  5. Peisong Han & Linglong Kong & Jiwei Zhao & Xingcai Zhou, 2019. "A general framework for quantile estimation with incomplete data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 81(2), pages 305-333, April.
  6. Sixia Chen & David Haziza, 2017. "Multiply robust imputation procedures for zero-inflated distributions in surveys," METRON, Springer;Sapienza Università di Roma, vol. 75(3), pages 333-343, December.
  7. Han, Peisong & Song, Peter X.-K. & Wang, Lu, 2015. "Achieving semiparametric efficiency bound in longitudinal data analysis with dropouts," Journal of Multivariate Analysis, Elsevier, vol. 135(C), pages 59-70.
  8. Peisong Han, 2014. "Multiply Robust Estimation in Regression Analysis With Missing Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1159-1173, September.
  9. Sho Komukai & Satoshi Hattori, 2017. "Doubly robust estimator for net survival rate in analyses of cancer registry data," Biometrics, The International Biometric Society, vol. 73(1), pages 124-133, March.
  10. Shixiao Zhang & Peisong Han & Changbao Wu, 2023. "Calibration Techniques Encompassing Survey Sampling, Missing Data Analysis and Causal Inference," International Statistical Review, International Statistical Institute, vol. 91(2), pages 165-192, August.
  11. Zhong Guan & Jing Qin, 2017. "Empirical likelihood method for non-ignorable missing data problems," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(1), pages 113-135, January.
  12. Xiang Zhou, 2022. "Semiparametric estimation for causal mediation analysis with multiple causally ordered mediators," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(3), pages 794-821, July.
  13. Fei Gao & K. C. G. Chan, 2023. "Noniterative adjustment to regression estimators with population‐based auxiliary information for semiparametric models," Biometrics, The International Biometric Society, vol. 79(1), pages 140-150, March.
  14. Satoshi Hattori & Masayuki Henmi, 2014. "Stratified doubly robust estimators for the average causal effect," Biometrics, The International Biometric Society, vol. 70(2), pages 270-277, June.
  15. Lucia Babino & Andrea Rotnitzky & James Robins, 2019. "Multiple robust estimation of marginal structural mean models for unconstrained outcomes," Biometrics, The International Biometric Society, vol. 75(1), pages 90-99, March.
  16. Wang, Qihua & Su, Miaomiao & Wang, Ruoyu, 2021. "A beyond multiple robust approach for missing response problem," Computational Statistics & Data Analysis, Elsevier, vol. 155(C).
  17. Chixiang Chen & Biyi Shen & Aiyi Liu & Rongling Wu & Ming Wang, 2021. "A multiple robust propensity score method for longitudinal analysis with intermittent missing data," Biometrics, The International Biometric Society, vol. 77(2), pages 519-532, June.
  18. Changbao Wu & Shixiao Zhang, 2019. "Comments on: Deville and Särndal’s calibration: revisiting a 25 years old successful optimization problem," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(4), pages 1082-1086, December.
  19. Peisong Han, 2016. "Combining Inverse Probability Weighting and Multiple Imputation to Improve Robustness of Estimation," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(1), pages 246-260, March.
  20. Hamori, Shigeyuki & Motegi, Kaiji & Zhang, Zheng, 2019. "Calibration estimation of semiparametric copula models with data missing at random," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 85-109.
  21. Matthew Cefalu & Francesca Dominici & Nils Arvold & Giovanni Parmigiani, 2017. "Model averaged double robust estimation," Biometrics, The International Biometric Society, vol. 73(2), pages 410-421, June.
  22. Xiaogang Duan & Guosheng Yin, 2017. "Ensemble Approaches to Estimating the Population Mean with Missing Response," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(4), pages 899-917, December.
  23. Kwun Chuen Gary Chan & Sheung Chi Phillip Yam & Zheng Zhang, 2016. "Globally efficient non-parametric inference of average treatment effects by empirical balancing calibration weighting," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(3), pages 673-700, June.
  24. Zhuoer Sun & Suojin Wang, 2019. "Semiparametric estimation in regression with missing covariates using single-index models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(5), pages 1201-1232, October.
  25. Benjamin Gochanour & Sixia Chen & Laura Beebe & David Haziza, 2023. "A semiparametric multiply robust multiple imputation method for causal inference," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 86(5), pages 517-542, July.
  26. Chen, Sixia & Haziza, David, 2018. "Jackknife empirical likelihood method for multiply robust estimation with missing data," Computational Statistics & Data Analysis, Elsevier, vol. 127(C), pages 258-268.
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