Limit Properties of the Largest Entries of High-Dimensional Sample Covariance and Correlation Matrices | Zendy

Xue Ding | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Limit Properties of the Largest Entries of High-Dimensional Sample Covariance and Correlation Matrices

Author(s) -

Xue Ding

Publication year - 2021

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2021/9635202

Subject(s) - mathematics , limit (mathematics) , covariance , independent and identically distributed random variables , sample (material) , covariance matrix , dimension (graph theory) , sample size determination , statistics , population , law of total covariance , distribution (mathematics) , central limit theorem , estimation of covariance matrices , combinatorics , random variable , mathematical analysis , covariance intersection , physics , demography , sociology , thermodynamics

In this paper, we consider the limit properties of the largest entries of sample covariance matrices and the sample correlation matrices. In order to make the statistics based on the largest entries of the sample covariance matrices and the sample correlation matrices more applicable in high-dimensional tests, the identically distributed assumption of population is removed. Under some moment’s assumption of the underlying distribution, we obtain that the almost surely limit and asymptotical distribution of the extreme statistics as both the dimension p and sample size n tend to infinity.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research