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 mode...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.展开更多
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)+1 种基金Jilin Provincial Science and Technology Development Plan Funded Project(Grant No.20180520026JH)the National Institute of Health。
文摘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.
