Alternatively, you can directly specify the matrix A to find the D-optimal design when A is the variance-covariance matrix for the runs. You can specify the data set containing the covariance matrix ...

For example, a downward move of the S&P 500 leads to an increased average correlation between stocks. Here again, RMT provides powerful tools to describe the time evolution of the full covariance ...

The covariance matrix of the observed variables Y and X has only three elements that are free to vary, since Cov (Y,X)=Cov (X,Y). The covariance structure can, therefore, be expressed as three ...

Tony Cai, Weidong Liu, Yin Xia, Two-Sample Covariance Matrix Testing and Support Recovery in High-Dimensional and Sparse Settings, Journal of the American Statistical Association, Vol. 108, No. 501 ...

This paper proposes a novel shrinkage estimator for high-dimensional covariance matrices by extending the Oracle Approximating Shrinkage (OAS) of Chen et al. (2009) to target the diagonal elements of ...

The purpose of The Annals of Probability is to publish contributions to the theory of probability and statistics and their applications. The emphasis is on importance and interest; formal novelty and ...

Covariance matrix estimation, crucial for multivariate inference, faces significant challenges when the number of variables rivals or exceeds the sample size.

We compute cosine similarity measures to compare risk factor reports and use them in linear regressions to estimate the covariance matrix of stock returns. Our estimators using risk report data ...