5 papers
Bias-Corrected Multiplier Bootstrap Inference for Spectral Edges of Large Covariance Matrices
Xiucai Ding, Yichen Hu, Jiahui Xie
Inference for spectral edges of large covariance matrices is a fundamental problem in high-dimensional statistics. A major difficulty is that the largest non-spiked sample eigenval…
The logarithmic law of sample correlation matrices
Yanpeng Li, Zhi Liu, Jiahui Xie +1
Let be the sample correlation matrix constructed from , whose entries are independent and identically distributed random variable…
The Spurious Factor Dilemma: Robust Inference in Heavy-Tailed Elliptical Factor Models
Jiang Hu, Jiahui Xie, Yangchun Zhang +1
Standard methods for determining the number of factors often overestimate the true number when data exhibit heavy-tailed randomness, misinterpreting noise-induced outliers as genui…
On Convergence Rates of Spiked Eigenvalue Estimates: A General Study of Global and Local Laws in Sample Covariance Matrices
Bing-Yi Jing, Weiming Li, Jiahui Xie +2
This paper investigates global and local laws for sample covariance matrices with general growth rates of dimensions. The sample size and population dimension can have the…
Necessary and sufficient condition for CLT of linear spectral statistics of sample correlation matrices
Yanpeng Li, Guangming Pan, Jiahui Xie +1
In this paper, we establish the central limit theorem (CLT) for the linear spectral statistics (LSS) of sample correlation matrix , constructed from a data matrix $X…