activity
20162026
most citedCentral limit theorem for linear spectral statistics of large dimensional separable sample covariance matrices

2 citations · 4 across the 16 of their papers we have counts for

collaborators

16 papers

math.PR2026

Local Laws and Edge Universality for Noncentral Sample Covariance Matrices

Can Hu, Jiang Hu, Zhidong Bai

We consider the real noncentral sample covariance matrices with . Here is deterministic, $Σ\in \mathbb{R}^{M\times…

math.ST2026

Functional CLT for general sample covariance matrices

Jian Cui, Zhijun Liu, Jiang Hu +1

This paper studies the central limit theorems (CLTs) for linear spectral statistics (LSSs) of general sample covariance matrices, when the test functions belong to , the class…

math.ST2025

Asymptotics for Reinforced Stochastic Processes on Hierarchical Networks

Li Yang, Dandan Jiang, Jiang Hu +1

In this paper, we analyze the asymptotic behavior of a system of interacting reinforced stochastic processes on a directed network of agents. The sys…

math.PR2025

A general partial Cramér's condition for Edgeworth expansion of a function of sample means with applications

Yashi Wei, Jiang Hu, Zhidong Bai

A large class of statistics can be formulated as smooth functions of sample means of random vectors. In this paper, we propose a general partial Cramér's condition (GPCC) and apply…

math.ST2025

Asymptotic distributions of four linear hypotheses test statistics under generalized spiked model

Zhijun Liu, Jiang Hu, Zhidong Bai +1

In this paper, we establish the Central Limit Theorem (CLT) for linear spectral statistics (LSSs) of large-dimensional generalized spiked sample covariance matrices, where the spik…

math.ST2025

Edgeworth corrections for the spiked eigenvalues of non-Gaussian sample covariance matrices with applications

Yashi Wei, Jiang Hu, Zhidong Bai

Yang and Johnstone (2018) established an Edgeworth correction for the largest sample eigenvalue in a spiked covariance model under the assumption of Gaussian observations, leaving…