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09/02/2023
Robust estimation for number of factors in high dimensional factor modeling via Spearman correlation matrix
Determining the number of factors in high-dimensional factor modeling is...
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04/06/2023
Heavy-Tailed Regularization of Weight Matrices in Deep Neural Networks
Unraveling the reasons behind the remarkable success and exceptional gen...
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02/06/2023
Network Autoregression for Incomplete Matrix-Valued Time Series
We study the dynamics of matrix-valued time series with observed network...
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07/06/2022
Self-Constrained Inference Optimization on Structural Groups for Human Pose Estimation
We observe that human poses exhibit strong group-wise structural correla...
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02/25/2022
On singular values of large dimensional lag-tau sample autocorrelation matrices
We study the limiting behavior of singular values of a lag-Ï„ sample auto...
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09/28/2021
On eigenvalues of a high dimensional Kendall's rank correlation matrix with dependences
This paper investigates limiting spectral distribution of a high-dimensi...
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09/14/2021
Asymptotic normality for eigenvalue statistics of a general sample covariance matrix when p/n →∞ and applications
The asymptotic normality for a large family of eigenvalue statistics of ...
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06/18/2021
Sparse Linear Spectral Unmixing of Hyperspectral images using Expectation-Propagation
This paper presents a novel Bayesian approach for hyperspectral image un...
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08/14/2020
Provable More Data Hurt in High Dimensional Least Squares Estimator
This paper investigates the finite-sample prediction risk of the high-di...
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12/13/2019
Central Limit Theorem for Linear Spectral Statistics of Large Dimensional Kendall's Rank Correlation Matrices and its Applications
This paper is concerned with the limiting spectral behaviors of large di...
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06/23/2019
Asymptotic joint distribution of extreme eigenvalues and trace of large sample covariance matrix in a generalized spiked population model
This paper studies the joint limiting behavior of extreme eigenvalues an...
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08/10/2018
On testing for high-dimensional white noise
Testing for white noise is a classical yet important problem in statisti...
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01/20/2018
