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Easy Question on Correlated Random Variables?
Posted:
Jun 17, 1996 6:11 PM
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Hi All,
I have a question regarding generaton of correlated random variables,
where each component can have a different distribution
(e.g. in <x1,x2,x3> x1 could be normal, x2 uniform and x3 beta).
One of papers (not a statistical journal mind you, so could be wrong)
mentions the following procedure to proceed about it.
1) From the covariance matrix generate desired number of random
vectors from multivariate normal distribution.
2) Evaluate cummulative distribution function (assuming the marginal
distribution is normal) for each observation in each random vector.
This step will produce vectors of correlated observations distributed
uniformly between 0,1 U(0,1).
3) Now substitute each component of the U(0,1) vector from step 3,
into the appropriate inverse marginal distribution function to get
vectors with observations of random variables, that have the desired
marginal distributions and similar correlation structure.
Even the procedure looks right, I haven't come across many instances of
usage of this kind of procedure. Is the above procedure correct, if not
is there a alternative method? Please reply. I would greatly appreciate
any comments in this regards.
Thanks in advance
Sudhakar
--
Sudhakar Mamillapalli
Dept Of Agricultural Engineering
Purdue University
sudhakar@ecn.purdue.edu
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