A note on statistical analysis of factor models of high dimension  

作　　者：Zhigen Gao Jianhua Guo Yanyuan Ma 

机构地区：[1]KLAS and School of Mathematics and Statistics,Northeast Normal University,Changchun 130024,China [2]Department of Statistics,Pennsylvania State University,University Park,PA 16802,USA

出　　处：《Science China Mathematics》2021年第8期1905-1916,共12页中国科学：数学（英文版）

基　　金：supported by National Natural Science Foundation of China(Grant Nos.11631003,11690012 and 11571068);the Fundamental Research Funds for the Central Universities(Grant No.2412019FZ030);Jilin Provincial Science and Technology Development Plan Funded Project(Grant No.20180520026JH);the National Institute of Health。

摘　　要：Linear factor models are familiar tools used in many fields.Several pioneering literatures established foundational theoretical results of the quasi-maximum likelihood estimator for high-dimensional linear factor models.Their results are based on a critical assumption:The error variance estimators are uniformly bounded in probability.Instead of making such an assumption,we provide a rigorous proof of this result under some mild conditions.

关 键 词：bounded in probability HETEROSCEDASTICITY high-dimensional linear factor model 

分 类 号：O212.1[理学—概率论与数理统计]