Biased-sample empirical likelihood weighting for missing data problems: an alternative to inverse probability weighting
缺失数据的偏样本经验似然加权:逆概率加权的替代方法
Abstract Inverse probability weighting (IPW) is widely used in many areas when data are subject to unrepresentativeness, missingness, or selection bias. An inevitable challenge with the use of IPW is that the IPW estimator can be remarkably unstable if some probabilities are very close to zero. To overcome this problem, at least three remedies have been developed in the literature: stabilizing, thresholding, and trimming. However, the final estimators are still IPW-type…