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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| Main Authors: | , , |
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| Format: | text |
| Language: | English |
| Published: |
Institutional Knowledge at Singapore Management University
2022
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| Subjects: | |
| Online Access: | 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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| Institution: | Singapore Management University |
| Language: | English |