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Abstract Details
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Activity Number:
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305
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Survey Research Methods
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| Abstract - #303315 |
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Title:
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Estimation with Nonignorable Missing Covariates
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Author(s):
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Jiwei Zhao*+ and Jun Shao
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Companies:
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University of Wisconsin at Madison and University of Wisconsin
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Address:
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, , 53706,
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Keywords:
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Non-ignorable ;
missing mechanism ;
missing covariate ;
pseudo-likelihood ;
identifiability ;
asymptotic normality
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Abstract:
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Estimation based on data with non-ignorable missing covariates is considered in this paper. We first consider the case where the missing mechanism is nonparametric and the relation between the response and the covariates is of a parametric regression form. We propose a consistent estimator to the regression coefficient when the covariates have non-ignorable missing values. We then show that this estimation procedure can be applied to the case even when the response also has non-ignorable missing values. The identifiability and the asymptotic normality are also derived. Simulation studies are conducted to show the finite sample performance of the proposed estimators. A real data set from the National Health and Nutrition Examination Survey is analyzed.
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