A method for generating realistic correlation matrices

DOI10.1214/13-AOAS638zbMath1454.62021arXiv1106.5834MaRDI QIDQ386759

Mohammad Hasan, H. S. Yoon

Publication date: 10 December 2013

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1106.5834

zbMATH Keywords

eigenvaluesToeplitz matrixcorrelation matrixsimulating matricesWeyl inequalities

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Random matrices (algebraic aspects) (15B52) Toeplitz, Cauchy, and related matrices (15B05)

Related Items (11)

Entropy measure of credit risk in highly correlated markets ⋮ Sequential double cross-validation for assessment of added predictive ability in high-dimensional omic applications ⋮ A method for generating realistic correlation matrices ⋮ Simulating realistic correlation matrices for financial applications: correlation matrices with the Perron–Frobenius property ⋮ Probabilistic Forecast Reconciliation under the Gaussian Framework ⋮ Correlation matrices with average constraints ⋮ Semiparametric estimation of the high-dimensional elliptical distribution ⋮ A globally convergent variant of mid-point method for finding the matrix sign ⋮ Constructing a high-order globally convergent iterative method for calculating the matrix sign function ⋮ Combining dissimilarity matrices by using rank correlations ⋮ Algorithms for generating equivalent normalized correlation matrices of noisy random signals

Uses Software

• rda
• WGCNA
• scout
• dynamicTreeCut
• GCTA
• clusfind
• ParaKMeans

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Regularized linear discriminant analysis and its application in microarrays
• A method for generating realistic correlation matrices
• Generating random correlation matrices based on vines and extended onion method
• Generating random correlation matrices based on partial correlations
• Generating random \(\mathrm{AR}(p)\) and \(\mathrm{MA}(q)\) Toeplitz correlation matrices
• Introduction to large truncated Toeplitz matrices
• Numerically stable generation of correlation matrices and their factors
• Sparse Canonical Correlation Analysis with Application to Genomic Data Integration
• Covariance-Regularized Regression and Classification for high Dimensional Problems
• Bias-Corrected Diagonal Discriminant Rules for High-Dimensional Classification
• On Random Correlation Matrices
• A note on a method for generating points uniformly on n -dimensional spheres
• Generating Correlation Matrices
• Finding Groups in Data
• A General Framework for Weighted Gene Co-Expression Network Analysis
• Spectral Properties of Banded Toeplitz Matrices
• Shrinkage‐based Diagonal Discriminant Analysis and Its Applications in High‐Dimensional Data

This page was built for publication: A method for generating realistic correlation matrices