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 ...

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

We consider a p-dimensional time series where the dimension p increases with the sample size n. The resulting data matrix X follows a stochastic volatility model: each entry consists of a positive ...

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 ...

Each estimation method is based on finding parameter estimates that minimize a badness-of-fit function that measures the difference between the observed sample covariance matrix and the predicted ...

The distribution of the latent roots of the sample covariance matrix is studied when the parent population is nonnormal. Asymptotic expansions of the marginal and joint distributions of the sample ...

The following example demonstrates how you can use the ACECLUS procedure to obtain approximate estimates of the pooled within-cluster covariance matrix and to compute canonical variables for ...