通过斯皮尔曼相关矩阵对高维因子模型中的因子数量进行稳健估计

IF 3 1区数学 Q1 STATISTICS & PROBABILITY

Journal of the American Statistical Association Pub Date : 2024-09-20 DOI:10.1080/01621459.2024.2402565

Jiaxin Qiu, Zeng Li, Jianfeng Yao

引用次数: 0

摘要

在高维因子建模中确定因子个数至关重要，但也极具挑战性，尤其是当数据是重尾数据时。在本文中，我们介绍了一种新的估算器，它基于...

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Robust estimation for number of factors in high dimensional factor modeling via Spearman correlation matrix

Determining the number of factors in high-dimensional factor modeling is essential but challenging, especially when the data are heavy-tailed. In this paper, we introduce a new estimator based on t...

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来源期刊

Journal of the American Statistical Association 数学-统计学与概率论

CiteScore

7.50

自引率

8.10%

发文量

168

审稿时长

12 months

期刊介绍： Established in 1888 and published quarterly in March, June, September, and December, the Journal of the American Statistical Association ( JASA ) has long been considered the premier journal of statistical science. Articles focus on statistical applications, theory, and methods in economic, social, physical, engineering, and health sciences. Important books contributing to statistical advancement are reviewed in JASA . JASA is indexed in Current Index to Statistics and MathSci Online and reviewed in Mathematical Reviews. JASA is abstracted by Access Company and is indexed and abstracted in the SRM Database of Social Research Methodology.