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20052026
most citedLimit theorems for sample eigenvalues in a generalized spiked population model

165 citations · 636 across the 49 of their papers we have counts for

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15 papers · 1 filter

math.PR2021★ 7 cited

On spectral distribution of sample covariance matrices from large dimensional and large -fold tensor products

Benoît Collins, Jianfeng Yao, Wangjun Yuan

We study the eigenvalue distributions for sums of independent rank-one -fold tensor products of large -dimensional vectors. Previous results in the literature assume that $k=…

math.PR2021★ 1 cited

Central limit theorem for linear spectral statistics of block-Wigner-type matrices

Zhenggang Wang, Jianfeng Yao

Motivated by the stochastic block model, we investigate a class of Wigner-type matrices with certain block structures, and establish a CLT for the corresponding linear spectral sta…

math.PR2021

Recent advances on eigenvalues of matrix-valued stochastic processes

Jian Song, Jianfeng Yao, Wangjun Yuan

Since the introduction of Dyson's Brownian motion in early 1960's, there have been a lot of developments in the investigation of stochastic processes on the space of Hermitian matr…

math.PR2020★ 2 cited

On eigenvalue distributions of large auto-covariance matrices

Jianfeng Yao, Wangjun Yuan

In this article, we establish a limiting distribution for eigenvalues of a class of auto-covariance matrices. The same distribution has been found in the literature for a regulariz…

math.PR2020★ 1 cited

Ratio-consistent estimation for long range dependent Toeplitz covariance with application to matrix data whitening

Peng Tian, Jianfeng Yao

We consider a data matrix from a multivariate stationary process with a separable covariance function, where is a positive semi-definite…

math.PR2020★ 5 cited

Limiting distributions for eigenvalues of sample correlation matrices from heavy-tailed populations

Johannes Heiny, Jianfeng Yao

Consider a -dimensional population with iid coordinates in the domain of attraction of a stable distribution with index . Since the var…