Inference for heteroskedastic PCA with missing data
From MaRDI portal
Publication:6550970
DOI10.1214/24-aos2366zbMath1539.62185MaRDI QIDQ6550970
Jianqing Fan, Yuxin Chen, Yuling Yan
Publication date: 5 June 2024
Published in: The Annals of Statistics (Search for Journal in Brave)
principal component analysismissing dataconfidence regionsuncertainty quantificationsubspace estimationheteroskedastic data
Factor analysis and principal components; correspondence analysis (62H25) Estimation in multivariate analysis (62H12) Nonconvex programming, global optimization (90C26) Approximations to statistical distributions (nonasymptotic) (62E17) Missing data (62D10)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- On asymptotically optimal confidence regions and tests for high-dimensional models
- Asymptotic normality and optimalities in estimation of large Gaussian graphical models
- High-dimensional covariance matrix estimation with missing observations
- A general theory of hypothesis tests and confidence regions for sparse high dimensional models
- On the impact of predictor geometry on the performance on high-dimensional ridge-regularized generalized robust regression estimators
- High-dimensional regression with noisy and missing data: provable guarantees with nonconvexity
- Finite sample approximation results for principal component analysis: A matrix perturbation approach
- Rate-optimal perturbation bounds for singular subspaces with applications to high-dimensional statistics
- On the distribution of the largest eigenvalue in principal components analysis
- Confidence intervals for high-dimensional linear regression: minimax rates and adaptivity
- Robust high-dimensional factor models with applications to statistical machine learning
- Subspace estimation from unbalanced and incomplete data matrices: \({\ell_{2,\infty}}\) statistical guarantees
- Bridging convex and nonconvex optimization in robust PCA: noise, outliers and missing data
- Heteroskedastic PCA: algorithm, optimality, and applications
- Statistical inference for principal components of spiked covariance matrices
- Partial recovery for top-\(k\) ranking: optimality of MLE and suboptimality of the spectral method
- Near-optimal performance bounds for orthogonal and permutation group synchronization via spectral methods
- Efficient estimation of linear functionals of principal components
- Implicit regularization in nonconvex statistical estimation: gradient descent converges linearly for phase retrieval, matrix completion, and blind deconvolution
- Entrywise eigenvector analysis of random matrices with low expected rank
- Normal approximation and confidence region of singular subspaces
- High dimensional deformed rectangular matrices with applications in matrix denoising
- Spectral method and regularized MLE are both optimal for top-\(K\) ranking
- The two-to-infinity norm and singular subspace geometry with applications to high-dimensional statistics
- Optimal shrinkage of eigenvalues in the spiked covariance model
- Gradient descent with random initialization: fast global convergence for nonconvex phase retrieval
- A generalized solution of the orthogonal Procrustes problem
- Phase transition of the largest eigenvalue for nonnull complex sample covariance matrices
- On the principal components of sample covariance matrices
- Exact matrix completion via convex optimization
- Singular vector and singular subspace distribution for the matrix denoising model
- Asymmetry helps: eigenvalue and eigenvector analyses of asymmetrically perturbed low-rank matrices
- Nonconvex Phase Synchronization
- Confidence Intervals and Hypothesis Testing for High-Dimensional Regression
- On robust regression with high-dimensional predictors
- Guaranteed Matrix Completion via Non-Convex Factorization
- An $\ell_{\infty}$ Eigenvector Perturbation Bound and Its Application to Robust Covariance Estimation
- Asymptotic theory for estimating the singular vectors and values of a partially-observed low rank matrix with noise
- High-Dimensional Probability
- Spectral Algorithms for Tensor Completion
- Covariance Matrix Estimation With Non Uniform and Data Dependent Missing Observations
- Tackling Small Eigen-Gaps: Fine-Grained Eigenvector Estimation and Inference Under Heteroscedastic Noise
- Nonconvex Low-Rank Tensor Completion from Noisy Data
- Statistical Inferences of Linear Forms for Noisy Matrix Completion
- Entrywise Estimation of Singular Vectors of Low-Rank Matrices With Heteroskedasticity and Dependence
- Nonconvex Rectangular Matrix Completion via Gradient Descent Without ℓ₂,∞ Regularization
- Noisy Matrix Completion: Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimization
- Confidence Region of Singular Subspaces for Low-Rank Matrix Regression
- Inference and uncertainty quantification for noisy matrix completion
- Nonconvex Optimization Meets Low-Rank Matrix Factorization: An Overview
- Matrix Completion From a Few Entries
- Restricted strong convexity and weighted matrix completion: Optimal bounds with noise
- The Rotation of Eigenvectors by a Perturbation. III
- Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models
- Asymptotic Theory of Eigenvectors for Random Matrices With Diverging Spikes
- High-Dimensional Principal Component Analysis with Heterogeneous Missingness
- Inference for low-rank models
- The Lasso with general Gaussian designs with applications to hypothesis testing
- Uncertainty Quantification for Nonconvex Tensor Completion: Confidence Intervals, Heteroscedasticity and Optimality
This page was built for publication: Inference for heteroskedastic PCA with missing data