scientific article; zbMATH DE number 7164771
From MaRDI portal
Publication:5214268
zbMath1446.62209arXiv1607.03045MaRDI QIDQ5214268
Peter D. Hoff, Alexander M. Franks
Publication date: 7 February 2020
Full work available at URL: https://arxiv.org/abs/1607.03045
Title: zbMATH Open Web Interface contents unavailable due to conflicting licenses.
empirical BayesStiefel manifoldhigh-dimensional datagene expression datacovariance estimationlarge \(p\)small \(n\)spiked covariance model
Related Items (max. 100)
Reducing subspace models for large‐scale covariance regression ⋮ Semiparametric partial common principal component analysis for covariance matrices ⋮ Envelopes and principal component regression ⋮ Principal regression for high dimensional covariance matrices ⋮ Region-referenced spectral power dynamics of EEG signals: a hierarchical modeling approach
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- A note on fast envelope estimation
- A feasible method for optimization with orthogonality constraints
- On the convergence properties of the EM algorithm
- The positive false discovery rate: A Bayesian interpretation and the \(q\)-value
- Survey on distance metric learning and dimensionality reduction in data mining
- On the distribution of the largest eigenvalue in principal components analysis
- Good edit similarity learning by loss minimization
- Optimal shrinkage of eigenvalues in the spiked covariance model
- Worst-case violation of sampled convex programs for optimization with uncertainty
- High-dimensional graphs and variable selection with the Lasso
- Eigenvalues of large sample covariance matrices of spiked population models
- Geodesic Monte Carlo on Embedded Manifolds
- A Covariance Regression Model
- The Optimal Hard Threshold for Singular Values is <inline-formula> <tex-math notation="TeX">\(4/\sqrt {3}\) </tex-math></inline-formula>
- Covariance reducing models: An alternative to spectral modelling of covariance matrices
- Newton-Raphson and EM Algorithms for Linear Mixed-Effects Models for Repeated-Measures Data
- Some tests for common principal component subspaces in several groups
- Probabilistic Principal Component Analysis
- Spectral models for covariance matrices
- On the Asymptotic Behavior of the Sample Estimates of Eigenvalues and Eigenvectors of Covariance Matrices
- Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods
- A Hierarchical Eigenmodel for Pooled Covariance Estimation
- Two generalizations of the common principal component model
- Partial common principal component subspaces
- The Joint Graphical Lasso for Inverse Covariance Estimation Across Multiple Classes
- Stein's Estimation Rule and Its Competitors--An Empirical Bayes Approach
- Admissible Bayes Character of $T^2-, R^2-$, and Other Fully Invariant Tests for Classical Multivariate Normal Problems
This page was built for publication:
