Robust Model Averaging Method Based on LOF Algorithm  

作　　者：Fan Wang Kang You Guohua Zou 

机构地区：[1]School of Mathematical Sciences,Capital Normal University,Beijing 100048,P.R.China

出　　处：《Communications in Mathematical Research》2023年第3期386-413,共28页数学研究通讯（英文版）

基　　金：supported by the National Natural Science Foundation of China (Grant Nos.11971323,12031016).

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 models.However,most of the existing model averaging criteria do not consider the influence of outliers on the estimation procedures.The 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 covariates.Asymptotic optimality of the proposed robust model averaging estimator is derived under some regularity conditions.Further,we prove the consistency of the LOF-based weight estimator tending to the theoretically optimal weight vector.Numerical studies including Monte Carlo simulations and a real data example are provided to illustrate our proposed methodology.

关 键 词：OUTLIERS LOF algorithm robust model averaging asymptotic optimality CONSISTENCY 

分 类 号：O241.6[理学—计算数学]