[SciPy-User] Generalized least square on large dataset
Thu Mar 8 11:32:45 CST 2012
> I would use SVD or eigenvalue decomposition to get the transformation
> matrix. With reduced rank and dropping zero eigenvalues, I think, the
> transformation will just drop some observations that are redundant.
> Or for normal equations, use X pinv(V) X beta = X pinv(V) y which
> uses SVD inside and requires less work writing the code.
> I'm reasonably sure that I have seen the pinv used this way before.
> That still leaves going from similarity matrix to covariance matrix.
Yes, pinv() solved the compute problem (no errors anymore). I've also found
some papers describing how to get from a similarity matrix to correlation.
Do you maybe know, are p-values (from MSE calculation) fairly accurate this
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