mle_from_holq: Get MLE from output of 'holq'.

In dcgerard/tensr: Covariance Inference and Decompositions for Tensor Datasets

Get MLE from output of holq.

Description

From the output of holq, this function will calculate the MLEs for the component covariance matrices and for the total variation parameter.

Usage

mle_from_holq(holq_obj)

Arguments

holq_obj

The output returned from holq.

Details

The function simply takes the A[[i]] output of holq and returs A[[i]] %*% t(A[[i]]). The estimate of the total variation parameter is sqrt(sig ^ 2 / prod{p}), whre p is the vector of dimensions of the data array and sig is the output from holq.

Value

cov_mle A list of positive definite matrices. These are the MLEs for the component covariance matrices.

sig_mle A numeric. This is an estimate of the "standard deviation" form of the total variation parameter.

Author(s)

David Gerard.

References

Gerard, D., & Hoff, P. (2016). A higher-order LQ decomposition for separable covariance models. Linear Algebra and its Applications, 505, 57-84. https://doi.org/10.1016/j.laa.2016.04.033 http://arxiv.org/pdf/1410.1094v1.pdf

See Also

holq.

dcgerard/tensr documentation built on Oct. 4, 2022, 5:58 p.m.