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Activity Number:
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346
- Contributed Poster Presentations: Section on Nonparametric Statistics
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Type:
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Contributed
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Date/Time:
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Tuesday, August 9, 2022 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract #323609
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Title:
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Efficient Estimator of a Functional When Response Is Missing Not at Random
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Author(s):
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Samidha Sudhakar Shetty* and Yanyuan Ma and Jiwei Zhao
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Companies:
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Pennsylvania State University and Pennsylvania State University and University of Wisconsin-Madison
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Keywords:
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Missing data;
Missing not at random;
Semiparametrics;
Efficient
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Abstract:
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Missing data is common in data sets in every field of science. There are three types of missing data: Missing Completely at Random (MCAR), Missing at Random (MAR) and Missing Not at Random (MNAR). These can also be classified into two main categories: Ignorable (MCAR and MAR) and Nonignorable (MNAR). Most missing data research has been under the ignorable missingness condition. We discuss the nonignorable condition which is less well studied. We allow the propensity to be modeled by a semiparametric logistic relation where the dependence on covariates is unspecified. We propose an efficient estimator for the parameteric component in the propensity model and prove the consistency and efficacy of the estimator. We then move on to efficient estimation of outcome mean by deriving the semiparametric efficient influence function. This turns out to be a difficult task and the resulting influence function is extremely complex. We propose an alternative estimator for the functional and study its asymptotic properties in detail.
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Authors who are presenting talks have a * after their name.
