报告题目：RMA: Ranking Based on Model Averaging
报 告 人：张新雨 中国科学院数学与系统科学研究院
Ranking problems are commonly encountered in practical applications, including order priority ranking, wine quality ranking, and piston slap noise performance ranking. The responses of these ranking applications are often considered as continuous responses and there is uncertainty on which scoring function is used to model the responses. In this paper, we address scoring function uncertainty of continuous response ranking problems by proposing a Ranking Model Averaging (RMA) method. With a set of candidate models varied by scoring functions, RMA assigns weights for each model determined by a K-fold cross-validation criterion based on pairwise loss. We provide two main theoretical properties for RMA. First, we prove that the averaging ranking predictions of RMA are asymptotically optimal in achieving the lowest possible ranking risk. Second, we provide a bound on the difference between the empirical RMA weights and theoretical optimal ones, and show that RMA weights are consistent. Simulation results validate RMA superiority over competing methods in reducing ranking risk. Moreover, when applied to empirical examples-order priority, wine quality, and piston slap noise performance-RMA shows its effectiveness in building accurate ranking systems.
张新雨，中国科学院数学与系统科学研究院研究员。主要从事统计学和计量经济学的理论和应用研究工作，具体研究方向包括模型平均、管理统计、机器学习和经济预测等，担任SCI期刊Journal of Systems Science & Complexity (JSSC)领域主编和其他5个国内外重要期刊的编委，是管理科学与工程学会常务理事。