Abstract：There is aninnate human tendency, one might call it the "league table mentality," to construct rankings. Schools, hospitals, sports teams, movies, and myriad other objects are ranked even though their inherent multi-dimensionality would suggest that -- at best -- onlypartial orderings were possible. We consider a large class of elementary ranking problems in which we observe noisy, scalar measurements of merit for n objectsof potentially heterogeneous precision and are asked to select a group of the objects that are "most meritorious." The problem is naturally formulated in the compound decision framework of Robbins's (1956) empirical Bayes theory, but it also exhibits closeconnections to the recent literature on multiple testing. The nonparametric maximum likelihood estimator for mixture models (Kiefer and Wolfowitz (1956)) is employed to construct optimal ranking and selection rules. Performance of the rules is evaluated insimulations and an application to ranking U.S kidney dialysis centers. We will also discuss the application of Empirical Bayes method in teacher value added analysis. 

报告人简介

顾佳颖老师任职于加拿大多伦多大学经济学系，现任副教授。

她的研究方向包括经验贝叶斯，混合模型以及离散选择模型。她的研究成果在Econometrica, Journal of American Statistical Association, Journal of Econometrics, Journal of Business and EconomicStatistics 等杂志多次发表。现主持加拿大社会科学和人文科学研究理事会的三个资助项目。