Spiked separable covariance matrices and principal components
Spiked separable covariance matrices and principal components Page 1 The Annals of Statistics
2021, Vol. 49, No. 2, 1113–1138 https://doi.org/10.1214/20-AOS1995 © Institute of Mathematical …
2021, Vol. 49, No. 2, 1113–1138 https://doi.org/10.1214/20-AOS1995 © Institute of Mathematical …
Phase transition for the smallest eigenvalue of covariance matrices
In this paper, we study the smallest non-zero eigenvalue of the sample covariance matrices
S (Y)= YY∗, where Y=(y ij) is an M× N matrix with iid mean 0 variance N-1 entries. We …
S (Y)= YY∗, where Y=(y ij) is an M× N matrix with iid mean 0 variance N-1 entries. We …
High dimensional deformed rectangular matrices with applications in matrix denoising
X Ding - 2020 - projecteuclid.org
High dimensional deformed rectangular matrices with applications in matrix denoising Page 1
Bernoulli 26(1), 2020, 387–417 https://doi.org/10.3150/19-BEJ1129 High dimensional deformed …
Bernoulli 26(1), 2020, 387–417 https://doi.org/10.3150/19-BEJ1129 High dimensional deformed …
Two sample test for covariance matrices in ultra-high dimension
X Ding, Y Hu, Z Wang - Journal of the American Statistical …, 2024 - Taylor & Francis
In this article, we propose a new test for testing the equality of two population covariance
matrices in the ultra-high dimensional setting that the dimension is much larger than the …
matrices in the ultra-high dimensional setting that the dimension is much larger than the …
Edge universality of separable covariance matrices
F Yang - 2019 - projecteuclid.org
In this paper, we prove the edge universality of largest eigenvalues for separable covariance
matrices of the form Q:=A^1/2XBX^*A^1/2. Here X=(x_ij) is an n*N random matrix with …
matrices of the form Q:=A^1/2XBX^*A^1/2. Here X=(x_ij) is an n*N random matrix with …
A CLT for the LSS of large-dimensional sample covariance matrices with diverging spikes
Z Liu, J Hu, Z Bai, H Song - The Annals of Statistics, 2023 - projecteuclid.org
A CLT for the LSS of large-dimensional sample covariance matrices with diverging spikes
Page 1 The Annals of Statistics 2023, Vol. 51, No. 5, 2246–2271 https://doi.org/10.1214/23-AOS2333 …
Page 1 The Annals of Statistics 2023, Vol. 51, No. 5, 2246–2271 https://doi.org/10.1214/23-AOS2333 …
Tracy-Widom distribution for heterogeneous Gram matrices with applications in signal detection
Detection of the number of signals corrupted by high-dimensional noise is a fundamental
problem in signal processing and statistics. This paper focuses on a general setting where …
problem in signal processing and statistics. This paper focuses on a general setting where …
Optimally weighted PCA for high-dimensional heteroscedastic data
Modern data are increasingly both high-dimensional and heteroscedastic. This paper
considers the challenge of estimating underlying principal components from high …
considers the challenge of estimating underlying principal components from high …
Debiased regression adjustment in completely randomized experiments with moderately high-dimensional covariates
Completely randomized experiment is the gold standard for causal inference. When the
covariate information for each experimental candidate is available, one typical way is to …
covariate information for each experimental candidate is available, one typical way is to …
Convergence of eigenvector empirical spectral distribution of sample covariance matrices
Convergence of eigenvector empirical spectral distribution of sample covariance matrices
Page 1 The Annals of Statistics 2020, Vol. 48, No. 2, 953–982 https://doi.org/10.1214/19-AOS1832 …
Page 1 The Annals of Statistics 2020, Vol. 48, No. 2, 953–982 https://doi.org/10.1214/19-AOS1832 …