A note on statistical analysis of factor models of high dimension

Zhigen Gao; Jianhua Guo; Yanyuan Ma

doi:10.1007/s11425-019-1698-1

A note on statistical analysis of factor models of high dimension

Zhigen Gao
, Jianhua Guo
, Yanyuan Ma

Statistics

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

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.

Original language	English (US)
Pages (from-to)	1905-1916
Number of pages	12
Journal	Science China Mathematics
Volume	64
Issue number	8
DOIs	https://doi.org/10.1007/s11425-019-1698-1
State	Published - Aug 2021

All Science Journal Classification (ASJC) codes

General Mathematics

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10.1007/s11425-019-1698-1

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title = "A note on statistical analysis of factor models of high dimension",

abstract = "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.",

author = "Zhigen Gao and Jianhua Guo and Yanyuan Ma",

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AB - 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.

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A note on statistical analysis of factor models of high dimension

Abstract

All Science Journal Classification (ASJC) codes

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