Nonignorable missing data, single index propensity score and profile synthetic distribution function
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 assumpt...
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2022
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sg-smu-ink.soe_research-34672022-05-23T08:40:35Z Nonignorable missing data, single index propensity score and profile synthetic distribution function CHEN, Xuerong LEUNG, Denis H. Y. QIN, Jing 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 favorably with existing methods. 2022-02-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/2466 info:doi/10.1080/07350015.2020.1860065 https://ink.library.smu.edu.sg/context/soe_research/article/3467/viewcontent/Nonignorable_Missing_Data_av.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Missing not at random Nonignorable missing Pseudo-conditional likelihood Single index model Synthetic distribution Econometrics Economics |
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Missing not at random Nonignorable missing Pseudo-conditional likelihood Single index model Synthetic distribution Econometrics Economics |
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Missing not at random Nonignorable missing Pseudo-conditional likelihood Single index model Synthetic distribution Econometrics Economics CHEN, Xuerong LEUNG, Denis H. Y. QIN, Jing Nonignorable missing data, single index propensity score and profile synthetic distribution function |
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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 favorably with existing methods. |
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CHEN, Xuerong LEUNG, Denis H. Y. QIN, Jing |
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CHEN, Xuerong LEUNG, Denis H. Y. QIN, Jing |
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CHEN, Xuerong |
title |
Nonignorable missing data, single index propensity score and profile synthetic distribution function |
title_short |
Nonignorable missing data, single index propensity score and profile synthetic distribution function |
title_full |
Nonignorable missing data, single index propensity score and profile synthetic distribution function |
title_fullStr |
Nonignorable missing data, single index propensity score and profile synthetic distribution function |
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Nonignorable missing data, single index propensity score and profile synthetic distribution function |
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nonignorable missing data, single index propensity score and profile synthetic distribution function |
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Institutional Knowledge at Singapore Management University |
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2022 |
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https://ink.library.smu.edu.sg/soe_research/2466 https://ink.library.smu.edu.sg/context/soe_research/article/3467/viewcontent/Nonignorable_Missing_Data_av.pdf |
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