Adjusted Empirical Likelihood Estimation of Distribution Function and Quantile with Nonignorable Missing Data
Adjusted Empirical Likelihood Estimation of Distribution Function and Quantile with Nonignorable Missing Data
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DOI：
10.1007/s11424-018-6334-6
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发表时间：
2018-02
影响因子：
2.1
通讯作者：
Xianwen Ding;Niansheng Tang
中科院分区：
文献类型：
--
作者：
Xianwen Ding;Niansheng Tang
This paper considers the estimation problem of distribution functions and quantiles with nonignorable missing response data. Three approaches are developed to estimate distribution functions and quantiles, i.e., the Horvtiz-Thompson-type method, regression imputation method and augmented inverse probability weighted approach. The propensity score is specified by a semiparametric exponential tilting model. To estimate the tilting parameter in the propensity score, the authors propose an adjusted empirical likelihood method to deal with the over-identified system. Under some regular conditions, the authors investigate the asymptotic properties of the proposed three estimators for distribution functions and quantiles, and find that these estimators have the same asymptotic variance. The jackknife method is employed to consistently estimate the asymptotic variances. Simulation studies are conducted to investigate the finite sample performance of the proposed methodologies.
