JSTOR Daily

On Asymptotics of Eigenvectors of Large Sample Covariance Matrix

Let $\{X_{ij}\}$, i, j =..., be a double array of i.i.d. complex random variables with EX₁₁ = 0, E|X₁₁|² = 1 and E|X₁₁|⁴ < ∞, and let $A_{n}=\frac{1 ...
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 ...
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 ...
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.
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 ...
Simon Fraser University

Mixed Models Theory

This section provides an overview of a likelihood-based approach to general linear mixed models. This approach simplifies and unifies many common statistical analyses, including those involving ...
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 ...