Robust Model Averaging Method Based on LOF Algorithm
作者机构：School of Mathematical SciencesCapital Normal UniversityBeijing 100048P.R.China
出 版 物：《Communications in Mathematical Research》 (数学研究通讯（英文版）)
年 卷 期：2023年第39卷第3期
页 面：386-413页
核心收录：
学科分类：07[理学] 070102[理学-计算数学] 0701[理学-数学]
基 金：supported by the National Natural Science Foundation of China (Grant Nos.11971323 12031016)
主 题：Outliers LOF algorithm robust model averaging asymptotic optimality consistency
摘 要：Model averaging is a good alternative to model selection,which can deal with the uncertainty from model selection process and make full use of the information from various candidate ***,most of the existing model averaging criteria do not consider the influence of outliers on the estimation *** purpose of this paper is to develop a robust model averaging approach based on the local outlier factor(LOF)algorithm which can downweight the outliers in the *** optimality of the proposed robust model averaging estimator is derived under some regularity ***,we prove the consistency of the LOF-based weight estimator tending to the theoretically optimal weight *** studies including Monte Carlo simulations and a real data example are provided to illustrate our proposed methodology.
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