Re: running Person's correlation

[Francesco Potortì]

>> I have a signal (long) and a template (short, fixed).  I have to compute
>> the Pearson's correlation of the short signal with a sliding window of
>> the long signal.  This is a convolution where each sample is divided by
>> the (fixed) standard deviation of the short signal and the running
>> standard deviation of the long signal.
>>
>> The only loopless way I can think of is to compute a running sum, a
>> running sum of squares, and use them to compute a running standard
>> deviation to be multiplied with the convolution.  Any more
>> straightforward methods?

Markus Bergholz:
>cumsum ?

Yes, cumsum(x) is the basis for a running sum, and cumsum(x.*x) is the
basis for a running sum of squares; the running sums can be used to get
a running standard deviation.  I am writing a function to do just that.
I was wondering if there was a more straightforward way.

Thomas D. Dean:
>corr_test?  Maybe look at the code and see how it is done there?

What is corr_test?

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