Spectral methods for data science: A statistical perspective
Spectral methods have emerged as a simple yet surprisingly effective approach for
extracting information from massive, noisy and incomplete data. In a nutshell, spectral …
extracting information from massive, noisy and incomplete data. In a nutshell, spectral …
Anomaly detection for electricity consumption in cloud computing: framework, methods, applications, and challenges
L Feng, S Xu, L Zhang, J Wu, J Zhang, C Chu… - EURASIP Journal on …, 2020 - Springer
Driven by industrial development and the rising population, the upward trend of electricity
consumption is not going to curb. While the electricity suppliers make every endeavor to …
consumption is not going to curb. While the electricity suppliers make every endeavor to …
Singular vector and singular subspace distribution for the matrix denoising model
Singular vector and singular subspace distribution for the matrix denoising model Page 1
The Annals of Statistics 2021, Vol. 49, No. 1, 370–392 https://doi.org/10.1214/20-AOS1960 © …
The Annals of Statistics 2021, Vol. 49, No. 1, 370–392 https://doi.org/10.1214/20-AOS1960 © …
[HTML][HTML] Tracy-Widom at each edge of real covariance and MANOVA estimators
Z Fan, IM Johnstone - The annals of applied probability: an official …, 2022 - ncbi.nlm.nih.gov
We study the sample covariance matrix for real-valued data with general population
covariance, as well as MANOVA-type covariance estimators in variance components models …
covariance, as well as MANOVA-type covariance estimators in variance components models …
Principal components in linear mixed models with general bulk
Principal components in linear mixed models with general bulk Page 1 The Annals of Statistics
2021, Vol. 49, No. 3, 1489–1513 https://doi.org/10.1214/20-AOS2010 © Institute of …
2021, Vol. 49, No. 3, 1489–1513 https://doi.org/10.1214/20-AOS2010 © Institute of …
[HTML][HTML] Eigenvalue distributions of variance components estimators in high-dimensional random effects models
F Zhou, IM Johnstone - Annals of statistics, 2019 - ncbi.nlm.nih.gov
We study the spectra of MANOVA estimators for variance component covariance matrices in
multivariate random effects models. When the dimensionality of the observations is large …
multivariate random effects models. When the dimensionality of the observations is large …
Kronecker-product random matrices and a matrix least squares problem
Z Fan, R Ma - arxiv preprint arxiv:2406.00961, 2024 - arxiv.org
We study the eigenvalue distribution and resolvent of a Kronecker-product random matrix
model $ A\otimes I_ {n\times n}+ I_ {n\times n}\otimes B+\Theta\otimes\**\in\mathbb {C}^{n …
model $ A\otimes I_ {n\times n}+ I_ {n\times n}\otimes B+\Theta\otimes\**\in\mathbb {C}^{n …
Matrix means and a novel high-dimensional shrinkage phenomenon
Many statistical settings call for estimating a population parameter, most typically the
population mean, based on a sample of matrices. The most natural estimate of the …
population mean, based on a sample of matrices. The most natural estimate of the …