Multiply Robust Estimation in Regression Analysis With Missing Data

By Peisong Han. 2014

158 citations recorded by the source corpus.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/b6ef6312050ab7a4c6b4de1c3f089c527456caf9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistics and Probability

1. David Haziza
2. Kosuke Morikawa
3. Yutaka Kano
4. Sixia Chen
5. Jerald F. Lawless
6. Jae Kwang Kim
7. Peisong Han
8. Zhiqiang Tan

Citations in the corpus

• Combining Inverse Probability Weighting and Multiple Imputation to Improve Robustness of Estimation (2016, methodology): “A formal demonstration of this numerical advantage through simulation studies can be found in Han (2014a, 2014b).”
• Empirical Likelihood Estimation Using Auxiliary Summary Information with Different Covariate Distributions (2019, methodology): “The inner loop almost always converges (Chen, Sitter and Wu (2002); Han (2014)).”
• Semiparametric maximum likelihood estimation with data missing not at random (2017, methodology): “The proposed method is different from that of Riddles, Kim, & Im (2016) in two ways.”
• Multiply robust imputation procedures for the treatment of item nonresponse in surveys (2017, methodology): “We considered the simulation set-up of Kang & Schafer (2007), also used by Chan & Yam (2014) and Han (2014a), among others.”