International Monetary Fund

High-Dimensional Covariance Matrix Estimation: Shrinkage Toward a Diagonal Target

Download PDF More Formats on IMF eLibrary Order a Print Copy Create Citation This paper proposes a novel shrinkage estimator for high-dimensional covariance matrices by extending the Oracle ...
Simon Fraser University

Example 24.9: Optimal Design in the Presence of Covariance

The BLOCKS statement finds a design that maximizes the determinant |X'AX| of the treatment information matrix, where A depends on the block or covariate model. Alternatively, you can directly specify ...
JSTOR Daily

The eigenvalues of the sample covariance matrix of a multivariate heavy-tailed stochastic volatility model

This is a preview. Log in through your library . Abstract We consider a multivariate heavy-tailed stochastic volatility model and analyze the large-sample behavior of its sample covariance matrix. We ...
Nature

Graphical Models and Covariance Matrix Estimation

Graphical models provide a robust framework for representing the conditional independence structure between variables through networks, enabling nuanced insight into complex high-dimensional data.
Risk

The informativeness of risk factor disclosures: estimating the covariance matrix of stock returns using similarity measures

While risk factor disclosures in 10-K filings have been criticized by practitioners as generic and boilerplate, recent studies indicate that these risk reports can be informative. This study ...
JSTOR Daily

Accurate Critical Constants for the One-Sided Approximate Likelihood Ratio Test of a Normal Mean Vector When the Covariance Matrix Is Estimated

Tang, Gnecco, and Geller (1989, Biometrika 76, 577-583) proposed an approximate likelihood ratio (ALR) test of the null hypothesis that a normal mean vector equals a null vector against the ...