The Annals of Statistics, Vol. 30, No. 4 (Aug., 2002), pp. 1081-1102 (22 pages) This paper analyzes whether standard covariance matrix tests work when dimensionality is large, and in particular larger ...

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 ...

Investopedia contributors come from a range of backgrounds, and over 25 years there have been thousands of expert writers and editors who have contributed. Gordon Scott has been an active investor and ...

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

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 ...

The estimation of portfolio value-at-risk (VaR) requires a good estimate of the covariance matrix. As it is well known that a sample covariance matrix based on some historical rolling window is noisy ...

The estimated covariance matrix of the parameter estimates is computed as the inverse Hessian matrix, and for unconstrained problems it should be positive definite. If the final parameter estimates ...