Thanks for the helpful answers guys, I'll follow up on your responses. Art, I've already tried using a proc sql, and it seemed to accomplish the same thing, but also still with the 64k limitation. My problem is as follows: I have a large set of data which we can say represents empirical, or actual losses. I'd like to generate a large number of random variables from the distribution of these losses. Since this is a distribution which can't be described exactly using some of the built-in parametric distributions available in SAS, I'd like to use something like the svrtutil_percentile utility function. I begin by generating a large number of random uniform variables (rand('UNIFORM')), and will get results something like 0.10, 0.70, 0.60....etc. I'd like to translate those numbers in to the 10% quantile value, 70% quantile value. 60% quantile value, etc from the original set of empirical losses. To do this, at least using the percentile function, for each uniform random variable I need to pass the array of actual losses and array of edf values (calculated using proc severity). This becomes difficult when using something over 2000-3000 original loss observations because the size of the variable surpasses the 64k limit. My SAS knowledge is fairly limited so maybe there's some easy solution that is out there. In Matlab, which I am more familiar with, this would be fairly straightforward, just to define an entire column of some variable as an input array. In SAS it seems this is not so simple.
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