multi_stein_loss_cov: Calculate multiway Stein's loss from component covariance...
In tensr: Covariance Inference and Decompositions for Tensor Datasets

Description Usage Arguments Details Value Author(s) References See Also

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.

1	multi_stein_loss_cov(B, Sigma, b, sigma)

`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.

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.

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

David Gerard.

Gerard, D., & Hoff, P. (2015). Equivariant minimax dominators of the MLE in the array normal model. Journal of Multivariate Analysis, 137, 32-49. https://doi.org/10.1016/j.jmva.2015.01.020 http://arxiv.org/pdf/1408.0424.pdf

multi_stein_loss, get_equi_bayes.

tensr documentation built on May 2, 2019, 2:32 p.m.