[SciPy-User] Generalized least square on large dataset

[Peter Cimermančič]

Hi,

I'd like to linearly fit the data that were NOT sampled independently. I
came across generalized least square method:

b=(X'*V^(-1)*X)^(-1)*X'*V^(-1)*Y

X and Y are coordinates of the data points, and V is a "variance matrix".

The equation is Matlab format - I've tried solving problem there too, bit
it didn't work - but eventually I'd like to be able to solve problems like
that in python. The problem is that due to its size (1000 rows and
columns), the V matrix becomes singular, thus un-invertable. Any
suggestions for how to get around this problem? Maybe using a way of
solving generalized linear regression problem other than GLS?

Regards,

Peter
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