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POhow to generate pseudo-random positive definite matrix with constraints on the off-diagonal elements?
 

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    <p>The user wants to impose a unique, non-trivial, upper/lower bound on the correlation between every pair of variable in a var/covar matrix.</p> <p>For example: I want a variance matrix in which all variables have 0.9 > |rho(x_i,x_j)| > 0.6, rho(x_i,x_j) being the correlation between variables x_i and x_j.</p> <p>Thanks.</p> <hr> <p>Ok, something of a quick&amp;dirty solution has been found, still if anyone know of a more <em>exact</em> way to get there, it'll be welcome.</p> <hr> <p>I lost my original login, so i'm reposting the question under a new login. <a href="https://stackoverflow.com/questions/1037340/how-to-generate-pseudo-random-positive-definite-matrix-with-constraints-on-the-of">The previous iteration</a> got the following answer</p> <p>*you mean pseudo-random, that's the correct terminology for <em>semi</em> random – Robert Gould</p> <p>*Good point, but I think he meant semi pseudo-random (the pseudo is assumed when talking about computer randomness :-p) – fortran</p> <p>*With "correlation", do you mean "covariance"? – Svante</p> <p>*no, i really do mean correlation. I want to generate a positive definite matrix such that all the correlations have tighter than trivial bounds. – vak</p> <p>*See my answer. Do you insist that the sample correlations lie within the specified bounds, or just the population correlations that generate the sample? I do suggest an idea that may work if your problem is the former. – woodchips</p> <p>*woodship: no i'm afraid your solution will not work, please see my answer in the original threat (link above). Thanks.</p>
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