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Author: Qin, Jing
Resulting in 2 citations.
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Chen, Xuerong Leung, Denis Heng-Yan Qin, Jing |
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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. |
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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.
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Li, Pengfei Qin, Jing Liu, Yukun |
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Instability of Inverse Probability Weighting Methods and a Remedy for Nonignorable Missing Data Li, P., Qin, J. & Liu, Y. (2023) Instability of inverse probability weighting methods and a remedy for nonignorable missing data. Biometrics, 00, 1– 12. Also: https://doi.org/10.1111/biom.13881 Cohort(s): Children of the NLSY79 Publisher: Wiley Online Keyword(s): Methods/Methodology; Missing Data/Imputation; Peabody Picture Vocabulary Test (PPVT); Statistical Analysis Permission to reprint the abstract has not been received from the publisher. Inverse probability weighting (IPW) methods are commonly used to analyze nonignorable missing data (NIMD) under the assumption of a logistic model for the missingness probability. However, solving IPW equations numerically may involve nonconvergence problems when the sample size is moderate and the missingness probability is high. Moreover, those equations often have multiple roots, and identifying the best root is challenging. Therefore, IPW methods may have low efficiency or even produce biased results. We identify the pitfall in these methods pathologically: they involve the estimation of a moment-generating function (MGF), and such functions are notoriously unstable in general. As a remedy, we model the outcome distribution given the covariates of the completely observed individuals semiparametrically. After forming an induced logistic regression (LR) model for the missingness status of the outcome and covariate, we develop a maximum conditional likelihood method to estimate the underlying parameters. The proposed method circumvents the estimation of an MGF and hence overcomes the instability of IPW methods. Our theoretical and simulation results show that the proposed method outperforms existing competitors greatly. Two real data examples are analyzed to illustrate the advantages of our method. We conclude that if only a parametric LR is assumed but the outcome regression model is left arbitrary, then one has to be cautious in using any of the existing statistical methods in problems involving NIMD. |
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Bibliography Citation
Li, Pengfei, Jing Qin and Yukun Liu. "Instability of Inverse Probability Weighting Methods and a Remedy for Nonignorable Missing Data." Li, P., Qin, J. & Liu, Y. (2023) Instability of inverse probability weighting methods and a remedy for nonignorable missing data. Biometrics, 00, 1– 12. A.
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