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Empirical Likelihood for Estimating Equations with Nonignorably Missing Data

1Department of Statistics, Yunnan University, Kunming, China.
2Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Publication Date:
Stat Sin
Volume Number:
Issue Number:
Stat Sin. 2014 Apr 1;24(2):723-47.
PubMed ID:
Empirical likelihood, estimating equations, exponential tilting, imputation, kernel regression, nonignorable missing data
Appears in Collections:
R01 MH086633/MH/NIMH NIH HHS/United States
UL1 RR025747/RR/NCRR NIH HHS/United States
P01 CA142538/CA/NCI NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
R21 AG033387/AG/NIA NIH HHS/United States
Generated Citation:
Tang N., Zhao P., Zhu H. Empirical Likelihood for Estimating Equations with Nonignorably Missing Data. Stat Sin. 2014 Apr 1;24(2):723-47. PMID: 24976738. PMCID: PMC4071774.
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We develop an empirical likelihood (EL) inference on parameters in generalized estimating equations with nonignorably missing response data. We consider an exponential tilting model for the nonignorably missing mechanism, and propose modified estimating equations by imputing missing data through a kernel regression method. We establish some asymptotic properties of the EL estimators of the unknown parameters under different scenarios. With the use of auxiliary information, the EL estimators are statistically more efficient. Simulation studies are used to assess the finite sample performance of our proposed EL estimators. We apply our EL estimators to investigate a data set on earnings obtained from the New York Social Indicators Survey.