The following pages link to (Q4420195):
Displaying 50 items.
- Estimating structured high-dimensional covariance and precision matrices: optimal rates and adaptive estimation (Q5965313) (← links)
- Metropolized Knockoff Sampling (Q6044631) (← links)
- Noncentral Wishart matrices, asymptotic normality of vec and smooth statistics (Q6044805) (← links)
- Distributed inference for two‐sample <i>U</i>‐statistics in massive data analysis (Q6049782) (← links)
- Test for mean matrix in GMANOVA model under heteroscedasticity and non-normality for high-dimensional data (Q6050285) (← links)
- Asymptotic behavior of the distributions of eigenvalues for beta-Wishart ensemble under the dispersed population eigenvalues (Q6053881) (← links)
- Expressing the largest eigenvalue of a singular beta F-matrix with heterogeneous hypergeometric functions (Q6063731) (← links)
- Ridgelized Hotelling’s T<sup>2</sup> test on mean vectors of large dimension (Q6063738) (← links)
- Assessment of Covariance Selection Methods in High-Dimensional Gaussian Graphical Models (Q6066549) (← links)
- Simultaneous confidence tubes for comparing several multivariate linear regression models (Q6068467) (← links)
- Hypothesis testing in multivariate normal models with block circular covariance structures (Q6068488) (← links)
- Use of Random Integration to Test Equality of High Dimensional Covariance Matrices (Q6069480) (← links)
- Rates of Bootstrap Approximation for Eigenvalues in High-Dimensional PCA (Q6069877) (← links)
- Adaptive Tests for Bandedness of High-dimensional Covariance Matrices (Q6069890) (← links)
- A Universal Test on Spikes in a High-Dimensional Generalized Spiked Model and Its Applications (Q6069893) (← links)
- Bayesian decision rules to classification problems (Q6075114) (← links)
- Multivariate Kruskal_Wallis tests based on principal component score and latent source of independent component analysis (Q6075171) (← links)
- Minimum cost‐compression risk in principal component analysis (Q6075174) (← links)
- Robust PCA for high‐dimensional data based on characteristic transformation (Q6075186) (← links)
- The moderate deviation principles of likelihood ratio tests under alternative hypothesis (Q6077687) (← links)
- Hypothesis Testing of Matrix Graph Model with Application to Brain Connectivity Analysis (Q6079973) (← links)
- Simple strategies that generate bounded solutions for the multiple‐choice multi‐dimensional knapsack problem: a guide for OR practitioners (Q6080629) (← links)
- New precise model of studentized principal components (Q6082470) (← links)
- Classification by likelihood accordance functions (Q6082979) (← links)
- Classification of observations into von Mises-Fisher populations with unknown parameters (Q6083002) (← links)
- Lognormal Distributions and Geometric Averages of Symmetric Positive Definite Matrices (Q6086481) (← links)
- Interpoint Distance Classification of High Dimensional Discrete Observations (Q6086612) (← links)
- Quantile modeling through multivariate log‐normal/independent linear regression models with application to newborn data (Q6091671) (← links)
- Principal Components Analysis for Right Censored Data (Q6092957) (← links)
- Cross-Validated Loss-based Covariance Matrix Estimator Selection in High Dimensions (Q6094089) (← links)
- Finite sample \(t\)-tests for high-dimensional means (Q6097558) (← links)
- Tyler's and Maronna's M-estimators: non-asymptotic concentration results (Q6097559) (← links)
- A test for the identity of a high-dimensional correlation matrix based on the \(\ell_4\)-norm (Q6101694) (← links)
- Some correlation tests for vectors of large dimension (Q6106184) (← links)
- Hypothesis testing for independence given a blocked compound symmetric covariance structure in a high-dimensional setting (Q6106269) (← links)
- A Normality Test for High-dimensional Data Based on the Nearest Neighbor Approach (Q6107242) (← links)
- Testing and signal identification for two-sample high-dimensional covariances via multi-level thresholding (Q6108302) (← links)
- Flexible factor model for handling missing data in supervised learning (Q6113645) (← links)
- The Wishart distribution with two different degrees of freedom (Q6115516) (← links)
- Asymptotic standard errors of intraclass correlation coefficients for two-way model (Q6116986) (← links)
- Exact and approximate computation of critical values of the largest root test in high dimension (Q6116995) (← links)
- High-dimensional asymptotic expansion of the null distribution for Schott’s test statistic for complete independence of normal random variables (Q6118221) (← links)
- Moments of the likelihood-based discriminant function (Q6118235) (← links)
- Bayesian estimation of correlation matrices of longitudinal data (Q6120421) (← links)
- Universal Features for High-Dimensional Learning and Inference (Q6125994) (← links)
- A portmanteau local feature discrimination approach to the classification with high-dimensional matrix-variate data (Q6133725) (← links)
- The confidence density for correlation (Q6133734) (← links)
- Robust matrix estimations meet Frank-Wolfe algorithm (Q6134341) (← links)
- Estimation and classification using progressive type-II censored samples from two exponential populations with a common location (Q6134367) (← links)
- Testing the independence of variables for specific covariance structures: A simulation study (Q6143562) (← links)
