Calculate multiway Stein's loss from square root matrices.

Description

Given a list of estimates of the lower-triangular Cholesky square roots of component covariance matrices, a list of true lower-triangular Cholesky square roots of component covariance matrices, an estimate of the total variation, and the true total variation, multi_stein_loss will calculate multiway Stein's loss between the estimates and the truth.

Usage

1
multi_stein_loss(B, Psi, b, psi)

Arguments

B

A list of lower triangular matrices. These are the 'estimates' of the lower-triangular Cholesky square roots of the component covariance matrices.

Psi

A list of lower triangular matrices. These are the 'true' lower-triangular Cholesky square roots of the component covariance matrices.

b

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

psi

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_cov also calculates multiway Stein's loss, but uses the component covariance matrices (not the Cholesky roots) 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_cov, get_equi_bayes.

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