Biased-sample empirical likelihood weighting: an alternative to inverse probability weighting
报告人：Yukun Liu(East China Normal University)
地点：Tencent Meeting(785 121 633)
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 estimators, and inevitably inherit certain weaknesses of the naive IPW estimator: they may still be unstable or biased. We propose a biased-sample empirical likelihood weighting (ELW) method to serve the same general purpose as IPW, while completely overcoming the instability of IPW-type estimators by circumventing the use of inverse probabilities. The ELW weights are always well defined and easy to implement. We show theoretically that the ELW estimator is asymptotically normal and more efficient than the IPW estimator and its stabilized version for missing data problems and unequal probability sampling without replacement. Its asymptotic normality is also established under unequal probability sampling with replacement. Our simulation results and a real data analysis indicate that the ELW estimator is shift-equivariant, nearly unbiased, and usually outperforms the IPW-type estimators in terms of mean square error.
刘玉坤，华东师范大学统计学院教授，博士生导师，统计交叉科学研究院副院长，入选国家高层次青年人才计划。本科和博士毕业于南开大学统计系。研究兴趣包括经验似然和半参数统计理论及其在缺失数据、偏差数据、生态学、流行病学等方面的应用，在国内外重要统计期刊发表多篇科研论文。主持国家自然科学基金项目4项和科技部国家重点专项课题1项，参与重点项目2项；担任《应用概率统计》编委和责任编辑、《Statistical Theory and Related Fields》主编助理、以及《Journal of Applied Statistics》编委。
Meeting ID：785 121 633