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Author: Qin, Jing
Resulting in 1 citation.
1. Chen, Xuerong
Leung, Denis Heng-Yan
Qin, Jing
Non-ignorable Missing Data, Single Index Propensity Score and Profile Synthetic Distribution Function
Journal of Business and Economic Statistics published online (7 December 2020): DOI: 10.1080/07350015.2020.1860065.
Also: https://www.tandfonline.com/doi/full/10.1080/07350015.2020.1860065
Cohort(s): Children of the NLSY79
Publisher: American Statistical Association
Keyword(s): Missing Data/Imputation; Peabody Picture Vocabulary Test (PPVT); Propensity Scores

In missing data problems, missing not at random is difficult to handle since the response probability or propensity score is confounded with the outcome data model in the likelihood. Existing works often assume the propensity score is known up to a finite dimensional parameter. We relax this assumption and consider an unspecified single index model for the propensity score. A pseudo-likelihood based on the complete data is constructed by profiling out a synthetic distribution function that involves the unknown propensity score. The pseudo-likelihood gives asymptotically normal estimates. Simulations show the method compares favourably with existing methods.
Bibliography Citation
Chen, Xuerong, Denis Heng-Yan Leung and Jing Qin. "Non-ignorable Missing Data, Single Index Propensity Score and Profile Synthetic Distribution Function." Journal of Business and Economic Statistics published online (7 December 2020): DOI: 10.1080/07350015.2020.1860065.