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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Bibliographic Details
Main Authors: CHEN, Xuerong, LEUNG, Denis H. Y., QIN, Jing
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2022
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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
Description
Summary: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.