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Statistical inference for principal components of spiked covariance matrices

27 August 2020

Papers citing "Statistical inference for principal components of spiked covariance matrices"

28 / 28 papers shown

Title
High-Dimensional PCA Revisited: Insights from General Spiked Models and Data Normalization Effects Yanqing Yin Wang Zhou 41 1 0 25 Aug 2024
Uncertainty Quantification of Spectral Estimator and MLE for Orthogonal Group Synchronization Ziliang Samuel Zhong Shuyang Ling 62 1 0 12 Aug 2024
Sailing in high-dimensional spaces: Low-dimensional embeddings through angle preservation Jonas Fischer Rong Ma 79 0 0 14 Jun 2024
The Asymptotic Properties of the Extreme Eigenvectors of High-dimensional Generalized Spiked Covariance Model Zhangni Pu Xiaozhuo Zhang Jiang Hu Zhidong Bai 97 1 0 14 May 2024
Tests for principal eigenvalues and eigenvectors Jianqing Fan Yingying Li Ningning Xia Xinghua Zheng AIFin 77 0 0 11 May 2024
Detecting Spectral Breaks in Spiked Covariance Models Nina Dórnemann Debashis Paul 59 1 0 30 Apr 2024
Liberating dimension and spectral norm: A universal approach to spectral properties of sample covariance matrices Yanqing Yin 60 0 0 02 Jan 2024
Limiting behavior of bilinear forms for the resolvent of sample covariance matrices under elliptical distribution with applications Yanqing Yin Wang Zhou 17 0 0 27 Dec 2023
Global and local CLTs for linear spectral statistics of general sample covariance matrices when the dimension is much larger than the sample size with applications Xiucai Ding Zheng-G Wang 48 4 0 16 Aug 2023
Is your data alignable? Principled and interpretable alignability testing and integration of single-cell data Rong Ma Eric D. Sun D. Donoho James Zou 57 9 0 03 Aug 2023
Mode-wise Principal Subspace Pursuit and Matrix Spiked Covariance Model Runshi Tang M. Yuan Anru R. Zhang 93 3 0 02 Jul 2023
Deflated HeteroPCA: Overcoming the curse of ill-conditioning in heteroskedastic PCA Yuchen Zhou Yuxin Chen 71 4 0 10 Mar 2023
The Local Ledoit-Peche Law Van Latimer Benjamin D. Robinson 41 1 0 27 Feb 2023
Detection problems in the spiked matrix models Ji Hyung Jung Hye Won Chung J. Lee 117 2 0 12 Jan 2023
Extreme eigenvalues of Log-concave Ensemble Z. Bao Xiao‐Chuan Xu 49 1 0 22 Dec 2022
SIMPLE-RC: Group Network Inference with Non-Sharp Nulls and Weak Signals Jianqing Fan Yingying Fan Jinchi Lv Fan Yang 116 3 0 31 Oct 2022
Spectrally-Corrected and Regularized Linear Discriminant Analysis for Spiked Covariance Model Hua Li Wenya Luo Z. Bai Huanchao Zhou Zhangni Pu 53 2 0 08 Oct 2022
Theory of functional principal component analysis for discretely observed data Hang Zhou Dongyi Wei Fang Yao 71 2 0 19 Sep 2022
Distributed Learning for Principle Eigenspaces without Moment Constraints Yong He Zichen Liu Yalin Wang 83 0 0 29 Apr 2022
Learning Low-Dimensional Nonlinear Structures from High-Dimensional Noisy Data: An Integral Operator Approach Xiucai Ding Rongkai Ma 90 9 0 28 Feb 2022
Entrywise Recovery Guarantees for Sparse PCA via Sparsistent Algorithms Joshua Agterberg Jeremias Sulam 60 0 0 08 Feb 2022
Inference for Heteroskedastic PCA with Missing Data Yuling Yan Yuxin Chen Jianqing Fan 122 19 0 26 Jul 2021
Empirical Bayes PCA in high dimensions Xinyi Zhong Chang Su Z. Fan 86 19 0 21 Dec 2020
Limiting laws and consistent estimation criteria for fixed and diverging number of spiked eigenvalues Jian-bo Hu Jingfei Zhang Jianhua Guo Ji Zhu 65 1 0 15 Dec 2020
Impact of signal-to-noise ratio and bandwidth on graph Laplacian spectrum from high-dimensional noisy point cloud Xiucai Ding Hau‐Tieng Wu 107 13 0 21 Nov 2020
Eigenvector distribution in the critical regime of BBP transition Z. Bao Dong Wang 88 14 0 28 Sep 2020
Linear spectral statistics of eigenvectors of anisotropic sample covariance matrices Fan Yang 56 9 0 03 May 2020
Matrices with Gaussian noise: optimal estimates for singular subspace perturbation Sean O’Rourke Van Vu Ke Wang 72 7 0 02 Mar 2018