topic Re: Fit Log-gamma distribution in Statistical Procedures

Fit Log-gamma distribution

Lidz — Tue, 28 Jul 2015 08:18:34 GMT

Which procedure to use to fit Log-Gamma distribution?

Re: Fit Log-gamma distribution

Rick_SAS — Wed, 29 Jul 2015 14:51:37 GMT

If you are talking about fitting a parametric model to univariate data, you can

1) use PROC IML to fit the maximum likelihood estimates for the log-gamma model: http://blogs.sas.com/content/iml/2011/10/12/maximum-likelihood-estimation-in-sasiml.html

2) exponentiate and use PROC UNIVARIATE to fit a gamma distribution on the transformed data

Re: Fit Log-gamma distribution

StatDave — Sat, 01 Aug 2015 15:42:28 GMT

If you are asking how to fit a log-linear model to a gamma-distributed response variable, you can do that in PROC GLIMMIX or PROC GENMOD. For example, if Y is a gamma-distributed response, the following statements fit the model (with X1 and X2 as predictors) log(mean) = intercept + b1*x1 + b2*x2 :

proc genmod;

model y = x1 x2 / dist=gamma link=log;

run;

Re: Fit Log-gamma distribution

Lidz — Tue, 04 Aug 2015 09:55:03 GMT

Thanks for reply!

Since I want to simulate random numbers from log-gamma. Is it okay if I take the Log of obsv, fit gamma using Proc Univariate and exponentiate the simulated random numbers to get log-gamma rand. no.s?

Re: Fit Log-gamma distribution

Rick_SAS — Wed, 05 Aug 2015 13:19:15 GMT

Yes. The result to keep in mind is that if X is log-gamma distributed, then Y = log(X) has a gamma distribution. So yes, you can use PROC UNIVARIATE to fit the parameters for Y. Then you can use those parameters to simulate gamma data, and exponentiate those random variates to simulate the original data.

Re: Fit Log-gamma distribution

Lidz — Thu, 06 Aug 2015 06:02:07 GMT

Cool...thanks a lot!!!!!!