|Empirical Likelihood for Estimating Equations with Nonignorably Missing Data.
|Year of Publication
|Tang, Niansheng, Puying Zhao, and Hongtu Zhu
|2014 Apr 01
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.
|Empirical likelihood for estimating equations with nonignorably missing data.
|PubMed Central ID
|U54 EB005149 / EB / NIBIB NIH HHS / United States
R01 MH086633 / MH / NIMH NIH HHS / United States
UL1 RR025747 / RR / NCRR NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
R21 AG033387 / AG / NIA NIH HHS / United States