The following pages link to (Q5449216):
Displaying 50 items.
- Alternating direction method of multipliers for penalized zero-variance discriminant analysis (Q97537) (← links)
- Bi-cross-validation for factor analysis (Q104117) (← links)
- Integrative sparse principal component analysis (Q117095) (← links)
- Asymptotic properties of principal component analysis and shrinkage-bias adjustment under the generalized spiked population model (Q131450) (← links)
- Large-Sample Theory for the Bergsma-Dassios Sign Covariance (Q146217) (← links)
- Supervised singular value decomposition and its asymptotic properties (Q268716) (← links)
- On the Tracy-Widom approximation of Studentized extreme eigenvalues of Wishart matrices (Q272083) (← links)
- Adaptive shrinkage of singular values (Q294253) (← links)
- Kernel spectral clustering of large dimensional data (Q302428) (← links)
- Sparse PCA-based on high-dimensional Itô processes with measurement errors (Q321930) (← links)
- Sparse principal component analysis and iterative thresholding (Q355104) (← links)
- Limits of spiked random matrices. I (Q365716) (← links)
- Minimax bounds for sparse PCA with noisy high-dimensional data (Q366956) (← links)
- Additive/multiplicative free subordination property and limiting eigenvectors of spiked additive deformations of Wigner matrices and spiked sample covariance matrices (Q376265) (← links)
- Optimal detection of sparse principal components in high dimension (Q385763) (← links)
- Correlation tests for high-dimensional data using extended cross-data-matrix methodology (Q391612) (← links)
- Reconstruction of a low-rank matrix in the presence of Gaussian noise (Q391623) (← links)
- PCA consistency for the power spiked model in high-dimensional settings (Q391897) (← links)
- Learning a factor model via regularized PCA (Q399883) (← links)
- The largest eigenvalue of real symmetric, Hermitian and Hermitian self-dual random matrix models with rank one external source. I (Q411523) (← links)
- On the border of extreme and mild spiked models in the HDLSS framework (Q413760) (← links)
- Boundary behavior in high dimension, low sample size asymptotics of PCA (Q432317) (← links)
- Novel Fisher discriminant classifiers (Q437766) (← links)
- The singular values and vectors of low rank perturbations of large rectangular random matrices (Q444963) (← links)
- Batch latency analysis and phase transitions for a tandem of queues with exponentially distributed service times (Q475139) (← links)
- Detection, reconstruction, and characterization algorithms from noisy data in multistatic wave imaging (Q479102) (← links)
- Robust spiked random matrices and a robust G-MUSIC estimator (Q495368) (← links)
- On estimation in the reduced-rank regression with a large number of responses and predictors (Q495393) (← links)
- Cleaning large correlation matrices: tools from random matrix theory (Q521794) (← links)
- Considering Horn's parallel analysis from a random matrix theory point of view (Q525237) (← links)
- Asymptotics of the principal components estimator of large factor models with weakly influential factors (Q527936) (← links)
- Large panels with common factors and spatial correlation (Q530595) (← links)
- The eigenvalues and eigenvectors of finite, low rank perturbations of large random matrices (Q531808) (← links)
- Convergence and prediction of principal component scores in high-dimensional settings (Q620562) (← links)
- Central limit theorems for eigenvalues in a spiked population model (Q731681) (← links)
- A distance-based, misclassification rate adjusted classifier for multiclass, high-dimensional data (Q741160) (← links)
- Effective PCA for high-dimension, low-sample-size data with noise reduction via geometric representations (Q764487) (← links)
- On sample eigenvalues in a generalized spiked population model (Q765838) (← links)
- Principal components in linear mixed models with general bulk (Q820811) (← links)
- High-dimensional two-sample mean vectors test and support recovery with factor adjustment (Q830606) (← links)
- High-dimensional analysis of semidefinite relaxations for sparse principal components (Q834367) (← links)
- The largest sample eigenvalue distribution in the rank 1 quaternionic spiked model of Wishart ensemble (Q838000) (← links)
- The spectrum of kernel random matrices (Q847627) (← links)
- No eigenvalues outside the support of the limiting empirical spectral distribution of a separable covariance matrix (Q958905) (← links)
- Effective PCA for high-dimension, low-sample-size data with singular value decomposition of cross data matrix (Q990890) (← links)
- Covariance regularization by thresholding (Q1000302) (← links)
- Spectrum estimation for large dimensional covariance matrices using random matrix theory (Q1000306) (← links)
- Finite sample approximation results for principal component analysis: A matrix perturbation approach (Q1000307) (← links)
- Statistical eigen-inference from large Wishart matrices (Q1000310) (← links)
- Lasso-type recovery of sparse representations for high-dimensional data (Q1002157) (← links)
