Mallows Model Averaging Estimation for Linear Regression Model with Right Censored Data

作     者：Zhong-qi LIANG Xiao-lin CHEN Yan-qiu ZHOU Zhong-qi LIANG;Xiao-lin CHEN;Yan-qiu ZHOU

作者机构：School of Statistics and MathematicsZhejiang Gongshang UniversityHangzhou 310018China School of StatisticsQufu Normal UniversityQufu 273165China School of ScienceGuangxi University of Science and TechnologyLiuzhou 545006China 

出 版 物：《Acta Mathematicae Applicatae Sinica》 (应用数学学报（英文版）)

年 卷 期：2022年第38卷第1期

页      面：5-23页

核心收录：

学科分类：02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 070103[理学-概率论与数理统计] 0701[理学-数学] 

基　　金：supported by the Natural Science Foundation of Shandong Province of China(ZR2020MA023) Humanity and Social Science Research Foundation of Ministry of Education(MOE)of China(21YJA910002) Natural Science Foundation of Guangxi(2020AC19151) Middle-aged and Young Teachers’Basic Ability Promotion Project of Guangxi’Colleges and Universities(2021KY0343) 

主　　题：model averaging right censoring asymptotic optimality synthetic data 

摘      要：This paper is concerned with an optimal model averaging estimation for linear regression model with right censored data. The weights for model averaging are picked up via minimizing the Mallows criterion. Under some mild conditions, it is shown that the identified weights possess the property of asymptotic optimality, that is,the model averaging estimator corresponding to these weights achieves the lowest squared error *** numerical studies are conducted to evaluate the finite-sample performance of our method and make comparisons with its intuitive competitors, while an application to the PBC dataset is provided to serve as an illustration.