Empirical Likelihood Inference for the Mean Difference of Two Nonparametric Populations with Missing Data
Let Z = (X, Y)~T be a bivariate population with mean EZ = (EX,EY)~T and mean difference θ = EY-EX. Based on the complete data after inverse probability weighted (IPW) imputation, we make empirical likelihood (EL) inference for θ. It is shown that the limiting distribution of the EL statistic under IPW imputation is x_1~2 unlike the EL statistic based on the original nonparametric regression imputation approach which is asymptotically distributed as a scaled chi-squared distribution. This research has enhanced the EL method based on the original nonparametric regression imputation at two aspects: (1) it releases the burden of estimating adjustment coefficients; (2) it can improve the accuracy of the EL confidence intervals.
inverse probability weighted imputation empirical likelihood bootstrap confidence interval
QIN Yongsong QIU Tao LEI Qingzhu ZHANG Shichao
Department of Mathematics, Guangxi Normal University, P.R.China, 541004
国际会议
威海
英文
494-498
2010-07-24（万方平台首次上网日期，不代表论文的发表时间）