Relative Error Model Average for Multiplicative Models

发布时间：2025-12-08 作者：浏览次数：

Speaker:	夏小超	DateTime:	2025年12月11日（周四）上午10:00 - 11:00
Brief Introduction to Speaker: 夏小超, 重庆大学数学与统计学院副教授, 目前主要感兴趣的研究方向是海量数据下的分布式估计、子抽样和模型平均，相关工作发表在JoE、Biometrics、Statistica Sinica、JCGS、Statistics and Computing、CSDA等杂志，主持完成国家自然科学青年基金项目。		Place:	国交2号楼315会议室
Abstract:We propose a relative error model averaging (REMA) approach to predict positive response values under a set of multiplicative error models. To estimate the parameters in each candidate multiplicative model, we utilize a relative error loss as the empirical objective function. Specifically, we consider two commonly used loss functions: the least product relative error (LPRE) and the least absolute relative error (LARE), under which two model averaging estimators, REMA-LPRE and REMA-LARE, are proposed accordingly. The optimal weight vector is chosen by minimizing a jackknife version of the relative error loss. Theoretically, it is shown that under some technical conditions, our proposed model averaging estimators enjoy asymptotic optimality under the two losses, respectively, in the sense that its loss defined by a final prediction error (FPE) is asymptotically identical to that of the best yet infeasible model averaging estimator. Furthermore, we propose a model-based screening appro...