[SciPy-User] Generalized least square on large dataset
Wed Mar 7 20:39:17 CST 2012
I'd like to linearly fit the data that were NOT sampled independently. I
came across generalized least square method:
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?
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