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Re: Covariance Matrices
From: |
John Darrington |
Subject: |
Re: Covariance Matrices |
Date: |
Thu, 21 Aug 2008 20:29:32 +0800 |
User-agent: |
Mutt/1.5.18 (2008-05-17) |
On Wed, Aug 20, 2008 at 10:57:33AM -0400, Jason Stover wrote:
I had planned to add a one-pass algorithm, but used a two-pass algorithm
first because, "usually", two-pass algorithms have lower relative errors
than
one-pass algorithms, according to
"Algorithms for Computing the Sample Variance: Analysis and
Recommendations"
TF chan, G. Golub, R. LeVeque. American Statistician, v37 n3, 1983, pp.
242-247.
So I had planned to add a one-pass algorithm, based on the algorithms in
that
paper, but never got around to it.
My only suggestion is that the code in covariance-matrix.[ch] should have
both one- and two-pass algorithms. So maybe add covariance_accumulate()
and change covariance_pass_two () to incorporate your changes, but passing
an argument like double *means to use the means.
My opinion is that we should prefer speed rather than precision. So
all things being equal, I would use the single pass method. However,
in cases where there is a compelling reason to need another pass, then
the more accurate method can be used.
However, in many PSPP commands, the logic required to determine how
many passes are necessary is quite nasty; it can depend on exactly
which options are selected. For some time now, I've been thinking
of a scheme where each statistic is aware of its own dependencies.
With such a scheme, it would be possible to specify a set of
statistics, then the minimum number of passes would be automatically
determined, and the most accurate method for that number would be
automatically selected.
This scheme would take a bit of thought, and a lot of recoding. But
if the routines to calculate these statistics have a similar
interface, then that'll be the first step.
J'
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