The transformed data can be used for visualization or as the basis for prediction ... The first technique computes eigenvalues and eigenvectors from a covariance matrix derived from the source data.

When suppressing only active signals, this approach is able to provide a considerably higher fidelity in activity visualization ... is encoded in a special covariance matrix, which can be ...

The COV= option must be specified to compute an approximate covariance matrix for the parameter estimates under asymptotic theory for least-squares, maximum-likelihood, or Bayesian estimation, with or ...

(2009) to target the diagonal elements of the sample covariance matrix. We derive the closed-form solution of the shrinkage parameter and show by simulation that, when the diagonal elements of the ...

Markowitz’s optimal portfolio offers the best risk-reward trade-off – for a given set of predictors – but requires the covariance matrix of a potentially large pool of assets to be known and ...

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 ...