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10-23-2012 04:18 AM

Could someone kindly help with the following?

1. I would like to generate gamma distributed random variables using two parameters alpha(shape) and beta(scale). The call function randgen(gamVar,'GAMMA',alpha) sets beta=1 and hence uses one parameter. How can I generate with the two parameters in stead of assuming one of the parameters = 1?

2. How can I generate correlated/multivariate Gamma random variables? This can probably be done using the Wishart function call, right? How exactly?

Thanks for your help in advance,

George

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Solution

07-11-2017
09:09 AM

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Posted in reply to KGeorge

10-23-2012 08:20 AM

1) If X is a random variable with unit scale, beta*X is a random variable with scale parameter beta.

Therefore:

call randgen(gamVar,'GAMMA',alpha);

gamVar = beta*gamVar;

2) As it says in the doc for the Wishart distribution, there are several distributions that are known as "multivariate gamma."

The Wishart distribution is best known as the distribution for the covariance of a sample drawn from a MV normal.

If you require that the marginal distributions be univariate gamma (I do), then there are several options for multivariate gamma. For bivariate gamma, see p. 586-588 of Devroye's book, Non-Uniform Random Variate Generation

For higher dimensions, I'd probably use PROC COPULA

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Posted in reply to KGeorge

10-23-2012 08:19 AM

I got the solution to question 1 from SAS Data Steps under the section "Random numbers". The solution is x = b*rangam(seed,a); /* gamma with shape a & scale b */. Does anyone have a clue on question 2 above?

Solution

07-11-2017
09:09 AM

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Posted in reply to KGeorge

10-23-2012 08:20 AM

1) If X is a random variable with unit scale, beta*X is a random variable with scale parameter beta.

Therefore:

call randgen(gamVar,'GAMMA',alpha);

gamVar = beta*gamVar;

2) As it says in the doc for the Wishart distribution, there are several distributions that are known as "multivariate gamma."

The Wishart distribution is best known as the distribution for the covariance of a sample drawn from a MV normal.

If you require that the marginal distributions be univariate gamma (I do), then there are several options for multivariate gamma. For bivariate gamma, see p. 586-588 of Devroye's book, Non-Uniform Random Variate Generation

For higher dimensions, I'd probably use PROC COPULA

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Posted in reply to Rick_SAS

11-04-2012 12:50 AM

Thanks Rick for your reply. I am interested in the general case, bivariate or higher dimensional (indeed) with marginals being univariate gamma. Could you please illustrate with an example of how to implement this using proc copula?