Adjusted Empirical Likelihood Estimation of Distribution Function and Quantile with Nonignorable Missing Data

DING Xianwen; TANG Niansheng

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Adjusted Empirical Likelihood Estimation of Distribution Function and Quantile with Nonignorable Missing Data

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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.

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《系统科学与复杂性：英文版》 |2018年第3期|820-840|共21页
作者
DING Xianwen; TANG Niansheng;
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作者单位

Key Laboratory of Statistical Modeling & Data Analysis of Yunnan Province,Yunnan University,Kunming 650500,China;

Department of Mathematics,Jiangsu University of Technology,Changzhou 213001,China;

Key Laboratory of Statistical Modeling & Data Analysis of Yunnan Province,Yunnan University,Kunming 650500,China;

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Adjusted Empirical Likelihood Estimation of Distribution Function and Quantile with Nonignorable Missing Data

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