Timeline for Simulate a Gaussian Copula with t margins
Current License: CC BY-SA 3.0
6 events
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| Dec 5, 2015 at 6:27 | comment | added | Glen_b |
@whuber But nothing so complex is required to check the effect of qt -- each marginal will be uniform and we're only transforming the margins, so we only have to check the frequency with which qt(runif(...),3) has a problem.
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| Dec 5, 2015 at 0:21 | comment | added | whuber♦ | With df=3, I was seeing it relatively frequently. I didn't check, but it was on the order of a percent or so. I was using a minor variation of the OP's code, corrected to use the proper SD and to generate 6-D vectors rather than 50-D vectors (which are much harder to examine in detail!) | |
| Dec 4, 2015 at 20:50 | comment | added | Glen_b |
It looks like making use of lower.tail=FALSE in qt with a check for being in the upper half will make it an event that would happen less than once in a googol times for each tail. When I get back I'll look at whether to lay that out in detail.
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| Dec 4, 2015 at 20:39 | comment | added | Glen_b |
@whuber the question asks for t-margins, so I don't see how to avoid attempting some transform from normal to $t_3$; of course one might try to implement a more direct approach there. However, I just generated 10 million values from a uniform and transformed via qt without getting a single non-finite value returned. How often are you seeing it?
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| Dec 4, 2015 at 15:06 | comment | added | whuber♦ |
+1. Watch out, though: qt will produce infinite values especially with small degrees of freedom. The extreme t tails will make it practically impossible to gauge whether the resulting multivariate distribution follows a Gaussian copula. One should study the distribution of the uniform versions (in 2) rather than the distribution of the t variates (in 3).
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| Dec 4, 2015 at 10:05 | history | answered | Glen_b | CC BY-SA 3.0 |