Empirical likelihood-based inference under imputation for missing response data
Empirical likelihood-based inference under imputation for missing response data
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DOI：
10.1214/aos/1028674845
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发表时间：
2002-06
影响因子：
4.5
通讯作者：
Qihua Wang;J. Rao
中科院分区：
文献类型：
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作者：
Qihua Wang;J. Rao
考虑缺失响应数据的核回归插补下的推断。一个调整后的经验似然方法推断的平均响应变量的开发。通过证明调整后的经验对数似然比具有渐进标准卡方分布，证明了威尔克斯定理的非参数版本，并构造了相应的均值经验似然置信区间。利用辅助信息，定义了基于经验似然的估计量，并导出了调整后的经验对数似然比。证明了估计量的渐近正态性。此外，它表明，调整后的经验对数似然比服从威尔克斯定理。仿真研究进行了比较调整后的经验似然和正常的近似方法的覆盖精度和平均长度的置信区间。基于偏差和标准误差，通过模拟将经验似然估计与相关估计进行了比较。我们的模拟表明，调整后的经验似然法执行竞争力和辅助信息的使用提供了改进的推断。
Inference under kernel regression imputation for missing response data is considered. An adjusted empirical likelihood approach to inference for the mean of the response variable is developed. A nonparametric version of Wilks' theorem is proved for the adjusted empirical log-likelihood ratio by showing that it has an asymptotic standard chi-squared distribution, and the corresponding empirical likelihood confidence interval for the mean is constructed. With auxiliary information, an empirical likelihood-based estimator is defined and an adjusted empirical log-likelihood ratio is derived. Asymptotic normality of the estimator is proved. Also, it is shown that the adjusted empirical log-likelihood ratio obeys Wilks' theorem. A simulation study is conducted to compare the adjusted empirical likelihood and the normal approximation methods in terms of coverage accuracies and average lengths of confidence intervals. Based on biases and standard errors, a comparision is also made by simulation between the empirical likelihood-based estimator and related estimators. Our simulation indicates that the adjusted empirical likelihood method performs competitively and that the use of auxiliary information provides improved inferences.