Liu-West resampling: Infinite error in covariance estimation

[cgranade]

When covariance matrices are dominated by uncertainty in a low-dimensional subspace, scipy.linalg.sqrtm can sometimes report infinite error even if n_ess is large. I suspect that this can be solved by replacing sqrtm with a eigendecomposition that uses the hermicity of the covariance matrix, but this should be tested for accuracy.

[ihincks]

I have run into this problem, too.

I seem to recall that SVD algorithms are generally more stable than eigendecomposition algorithms, but I can't find a solid source, just things like the last page of these notes. I don't know if it matters in this context.