Ok, done!

]]>Terrific! You should also post the reply on StackOverflow, this answers my question!!!

]]>I’m not sure if the following helps with your question. Suppose that we have a multivariate normal random vector

with and $k\times k$ symmetric positive definite matrix .

For this lognormal we have

and it follows that .

So, we can ask the converse question: given and symmetric positive definite matrix , satisfying , if we let

we will have a lognormal vector with the prescribed means and covariances.

Regards,

Paulo.

P.S. The constraint on is equivalent to .

]]>I apologize.

One comment: Your R code does not seem to restrict the covariance of the two variables to be greater than -mu[1]mu[2]. This follows from Cov(X,Y) = E(XY)-E(X)E(Y) and the fact that XY is always nonnegative.

]]>Thank you: I was planning to post this question myself, and would have preferred to do so, but this is not a major problem! We will see if this attracts more answers than my original question.

]]>I suspect that someone there may be able to answer your initial question as well.

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