Pages that link to "Item:Q2477058"

The following pages link to Regularized estimation of large covariance matrices (Q2477058):

Displaying 50 items.

• Sparse vector Markov switching autoregressive models. Application to multivariate time series of temperature (Q1658459) (← links)
• A generalized likelihood ratio test for normal mean when \(p\) is greater than \(n\) (Q1659185) (← links)
• Comparison of linear shrinkage estimators of a large covariance matrix in normal and non-normal distributions (Q1659485) (← links)
• Sparse estimation of high-dimensional correlation matrices (Q1660228) (← links)
• Perturbations and projections of Kalman-Bucy semigroups (Q1660302) (← links)
• Joint estimation of multiple Gaussian graphical models across unbalanced classes (Q1662174) (← links)
• Confidence regions for entries of a large precision matrix (Q1668572) (← links)
• A nonparametric eigenvalue-regularized integrated covariance matrix estimator for asset return data (Q1668581) (← links)
• Performance analysis of local ensemble Kalman filter (Q1668684) (← links)
• Using principal component analysis to estimate a high dimensional factor model with high-frequency data (Q1676387) (← links)
• Asymptotic power of Rao's score test for independence in high dimensions (Q1715529) (← links)
• Spectral clustering via sparse graph structure learning with application to proteomic signaling networks in cancer (Q1727851) (← links)
• Consistency of large dimensional sample covariance matrix under weak dependence (Q1731211) (← links)
• Bootstrap -- an exploration (Q1731214) (← links)
• Posterior graph selection and estimation consistency for high-dimensional Bayesian DAG models (Q1731759) (← links)
• Knowing factors or factor loadings, or neither? Evaluating estimators of large covariance matrices with noisy and asynchronous data (Q1739632) (← links)
• A multiple testing approach to the regularisation of large sample correlation matrices (Q1739875) (← links)
• An extreme-value approach for testing the equality of large U-statistic based correlation matrices (Q1740532) (← links)
• Accuracy of regularized D-rule for binary classification (Q1747093) (← links)
• Large volatility matrix estimation with factor-based diffusion model for high-frequency financial data (Q1750098) (← links)
• Covariance estimation via sparse Kronecker structures (Q1750103) (← links)
• Gaussian and bootstrap approximations for high-dimensional U-statistics and their applications (Q1750282) (← links)
• Multivariate Student-\(t\) self-organizing maps (Q1784529) (← links)
• Factor-adjusted multiple testing of correlations (Q1796926) (← links)
• Robust covariance and scatter matrix estimation under Huber's contamination model (Q1800790) (← links)
• Weak convergence of the empirical spectral distribution of high-dimensional band sample covariance matrices (Q1800935) (← links)
• Discussion: Latent variable graphical model selection via convex optimization (Q1940763) (← links)
• Rejoinder: Latent variable graphical model selection via convex optimization (Q1940764) (← links)
• Adaptive covariance matrix estimation through block thresholding (Q1940765) (← links)
• Group symmetry and covariance regularization (Q1950873) (← links)
• Estimating networks with jumps (Q1950892) (← links)
• A dimension reduction based approach for estimation and variable selection in partially linear single-index models with high-dimensional covariates (Q1950897) (← links)
• Sparse permutation invariant covariance estimation (Q1951760) (← links)
• High dimensional sparse covariance estimation via directed acyclic graphs (Q1952020) (← links)
• Nonparametric estimation of covariance functions by model selection (Q1952083) (← links)
• Adaptive estimation of covariance matrices via Cholesky decomposition (Q1952094) (← links)
• High-dimensional covariance estimation by minimizing \(\ell _{1}\)-penalized log-determinant divergence (Q1952214) (← links)
• Optimal rates of convergence for estimating Toeplitz covariance matrices (Q1955842) (← links)
• Time varying undirected graphs (Q1959601) (← links)
• Weighted covariance matrix estimation (Q2002720) (← links)
• Semiparametric model for covariance regression analysis (Q2008100) (← links)
• Bayesian structure learning in graphical models (Q2018602) (← links)
• Nonparametric estimation of large covariance matrices with conditional sparsity (Q2024473) (← links)
• Model averaging prediction for time series models with a diverging number of parameters (Q2024480) (← links)
• Parameter-free robust optimization for the maximum-Sharpe portfolio problem (Q2030537) (← links)
• Estimation and optimal structure selection of high-dimensional Toeplitz covariance matrix (Q2034455) (← links)
• Estimation of autocovariance matrices for high dimensional linear processes (Q2036316) (← links)
• A novel change-point approach for the detection of gas emission sources using remotely contained concentration data (Q2044250) (← links)
• Functional data clustering by projection into latent generalized hyperbolic subspaces (Q2051583) (← links)
• Positive-definite modification of a covariance matrix by minimizing the matrix \(\ell_{\infty}\) norm with applications to portfolio optimization (Q2068898) (← links)