Journal of Applied Probability, Vol. 27, No. 1 (Mar., 1990), pp. 156-170 (15 pages) Let Xt be a discrete-time multivariate stationary process possessing an infinite autoregressive representation and ...
A Bayesian method is proposed for estimating an inverse covariance matrix from Gaussian data. The method is based on a prior that allows the off-diagonal elements of the inverse covariance matrix to ...
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 ...
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 ...
Within statistics, studying too many variables to find meaningful relationships among them is time consuming and expensive. Reducing dimensions (the number of variables) has been widely researched. A ...
Dozens of machine learning algorithms require computing the inverse of a matrix. Computing a matrix inverse is conceptually easy, but implementation is one of the most challenging tasks in numerical ...
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 ...
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