Hmm I'm not too sure
but the correlation coefficient must not be taken for its empirical counterpart
and the law of large numbers tells you roughly you can approximate a mean by
its empirical counterpart when the variables are identically distributed and
independant.
If they are not independant, the empirical counterpart could be a not very good
approximation.
Then I have learned it's always better to use the Spearman's rho or the other
one based on ranks, but not Pearson's correlation coefficient which is only the
best in the normal setting. There is a book from Lehmann about this
(Nonparametrics)
Date: Sun, 20 May 2007 23:03:26 -0400 From: [EMAIL PROTECTED] To:
r-help@stat.math.ethz.ch CC: [EMAIL PROTECTED] Subject: [R] Sample
correlation coefficient question NOT R question This is a statistics
question not an R question. When calculating the sample correlation
coefficient cor(x_t,y_t) between say two variables, x_t and y_t t=1,.n (
one can assume that the variables are in time but I don't think this really
matters for the question ), does someone know where I can find any piece of
literature that says that each (x_j,y_j) pair has To be independent from the
other (x_i,y_i) pairs (j doesn't equal i ) in order for the calculation to
have any reasonable meaning. This makes perfect sense to me but I need it
official writing so I can show it to someone else because I don't know how
to explain it. Obviously, there may be some way to calculate the
correlation coefficient when the (x_t,y_t) pairs aren't independent ( maybe
?) but I am referring to the very standard correlation calculation (
pearson for example or any other standard one ). Thanks for any
suggestions/references/insights etc.
This is not an
offer (or solicitation of an offer) to buy/se...{{dropped}}
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météo et bien plus encore !
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