The following pages link to (Q5449216):
Displaying 50 items.
- The largest eigenvalues of finite rank deformation of large Wigner matrices: Convergence and nonuniversality of the fluctuations (Q1011149) (← links)
- PCA consistency in high dimension, low sample size context (Q1043724) (← links)
- Concentration of measure and spectra of random matrices: applications to correlation matrices, elliptical distributions and beyond (Q1049567) (← links)
- On a spiked model for large volatility matrix estimation from noisy high-frequency data (Q1615279) (← links)
- Consistency of AIC and BIC in estimating the number of significant components in high-dimensional principal component analysis (Q1650069) (← links)
- Recent developments in high dimensional covariance estimation and its related issues, a review (Q1657856) (← links)
- Asymptotic performance of PCA for high-dimensional heteroscedastic data (Q1661372) (← links)
- Using principal component analysis to estimate a high dimensional factor model with high-frequency data (Q1676387) (← links)
- New asymptotic results in principal component analysis (Q1688427) (← links)
- Video denoising via empirical Bayesian estimation of space-time patches (Q1701995) (← links)
- Heterogeneity adjustment with applications to graphical model inference (Q1711558) (← links)
- \(e\)PCA: high dimensional exponential family PCA (Q1728639) (← links)
- The spectral norm of random inner-product kernel matrices (Q1729691) (← links)
- Canonical correlation coefficients of high-dimensional Gaussian vectors: finite rank case (Q1731774) (← links)
- Robust covariance estimation for approximate factor models (Q1739628) (← links)
- Asymptotics for empirical eigenvalue processes in high-dimensional linear factor models (Q1755119) (← links)
- Panel models with interactive effects (Q1792467) (← links)
- CLT for largest eigenvalues and unit root testing for high-dimensional nonstationary time series (Q1800798) (← links)
- Optimality and sub-optimality of PCA. I: Spiked random matrix models (Q1800806) (← links)
- On the maximal size of large-average and ANOVA-fit submatrices in a Gaussian random matrix (Q1940759) (← links)
- On finite rank deformations of Wigner matrices (Q1943320) (← links)
- Sparse permutation invariant covariance estimation (Q1951760) (← links)
- Partial generalized four moment theorem revisited (Q1983608) (← links)
- Extremal eigenvalues of sample covariance matrices with general population (Q1983634) (← links)
- Asymptotic joint distribution of extreme eigenvalues and trace of large sample covariance matrix in a generalized spiked population model (Q1996762) (← links)
- Roy's largest root under rank-one perturbations: the complex valued case and applications (Q2008213) (← links)
- Likelihood ratio test for partial sphericity in high and ultra-high dimensions (Q2011514) (← links)
- Robust high-dimensional factor models with applications to statistical machine learning (Q2038305) (← links)
- Subspace estimation from unbalanced and incomplete data matrices: \({\ell_{2,\infty}}\) statistical guarantees (Q2039795) (← links)
- Spiked separable covariance matrices and principal components (Q2039807) (← links)
- Universality of approximate message passing algorithms (Q2042851) (← links)
- Optimality of spectral clustering in the Gaussian mixture model (Q2054516) (← links)
- Eigenvector distribution in the critical regime of BBP transition (Q2073181) (← links)
- Optimal adaptivity of signed-polygon statistics for network testing (Q2073714) (← links)
- A guide for sparse PCA: model comparison and applications (Q2073736) (← links)
- Random matrix theory and its applications (Q2075698) (← links)
- Consistency of the objective general index in high-dimensional settings (Q2078579) (← links)
- Large sample correlation matrices: a comparison theorem and its applications (Q2082651) (← links)
- Limiting distribution of the sample canonical correlation coefficients of high-dimensional random vectors (Q2082707) (← links)
- Testing independence between two spatial random fields (Q2084410) (← links)
- Asymptotic independence of spiked eigenvalues and linear spectral statistics for large sample covariance matrices (Q2091835) (← links)
- An \({\ell_p}\) theory of PCA and spectral clustering (Q2091846) (← links)
- On the eigenvectors of large-dimensional sample spatial sign covariance matrices (Q2101471) (← links)
- CLT for spiked eigenvalues of a sample covariance matrix from high-dimensional Gaussian mean mixtures (Q2101482) (← links)
- Properties of eigenvalues and eigenvectors of large-dimensional sample correlation matrices (Q2108908) (← links)
- Relative perturbation bounds with applications to empirical covariance operators (Q2111217) (← links)
- Testing for the rank of a covariance operator (Q2112827) (← links)
- Heteroskedastic PCA: algorithm, optimality, and applications (Q2119219) (← links)
- Statistical inference for principal components of spiked covariance matrices (Q2131269) (← links)
- Perturbation theory for cross data matrix-based PCA (Q2140856) (← links)
