n*******r
发帖数: 60 | 1 My boss told me to calculate the correlation between Y and X
with cov(X,Y)/(std(X)*std(Y)),
but the trick is all the covariance and stds are calculated with zero mean.
Could anybody tell me what is the inisght behind it. Thanks! Bow<< | n*w
发帖数: 41 | 2 Is zero the real mean value of X and Y or you just use 0 when calculating
STD and COV?
If the mean is very close to zero compare to std, I guess you can use 0 to
simplify the calculation.
【在 n*******r 的大作中提到】
: My boss told me to calculate the correlation between Y and X
: with cov(X,Y)/(std(X)*std(Y)),
: but the trick is all the covariance and stds are calculated with zero mean.
: Could anybody tell me what is the inisght behind it. Thanks! Bow<<
| J*****n
发帖数: 4859 | 3
用log return
【在 n*******r 的大作中提到】
: My boss told me to calculate the correlation between Y and X
: with cov(X,Y)/(std(X)*std(Y)),
: but the trick is all the covariance and stds are calculated with zero mean.
: Could anybody tell me what is the inisght behind it. Thanks! Bow<<
| a*****n
发帖数: 20 | 4 In digital image processing, it is equivalent to removing the DC part of the
correlation images, which means ignoring the image background. Correlation
of two signals can be an evaluation of the similarity according to
their correlation peak values. However, if the two signals are similar in
shape but have completely different quantitative values, their correlation
will still turn out to be a peak, but you cannot judge the similarity
according to the peak value. This is because they are not unif
【在 n*******r 的大作中提到】
: My boss told me to calculate the correlation between Y and X
: with cov(X,Y)/(std(X)*std(Y)),
: but the trick is all the covariance and stds are calculated with zero mean.
: Could anybody tell me what is the inisght behind it. Thanks! Bow<<
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