Calculate multiway Stein's loss from component covariance matrices.

Share:

Description

Given a list of estimated component covariance matrices, a list of true component covariance matrices, an estimate of the total variation, and the true total variation, multi_stein_loss_cov will calculate multiway Stein's loss between the estimates and the truth.

Usage

1

Arguments

B

A list of positive definite matrices. These are the 'estimates' of the component covariance matrices.

Sigma

A list of positive definite matrices. These are the 'true' component covariance matrices.

b

A numeric. This is an 'estimate' of the total variation parameter, the 'standard devation' version of it.

sigma

A numeric. This is the 'true' total variation parameter, the 'standard devation' version of it.

Details

Multiway Stein's loss is a generalization of Stein's loss. More details on multiway Stein's loss and the Bayes rules under it can be found in Gerard and Hoff (2015).

The function multi_stien_loss also calculates multiway Stein's loss, but uses the lower-triangular Cholesky square roots of the component covariance matrices as input.

Value

A numeric, the multiway Stein's loss between the 'truth' and the 'estimates'.

Author(s)

David Gerard.

References

Gerard, D., & Hoff, P. (2015). Equivariant minimax dominators of the MLE in the array normal model. Journal of Multivariate Analysis, 137, 32-49.

See Also

multi_stein_loss, get_equi_bayes.

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.