Date:         Thu, 24 Jan 2008 16:43:17 -0800
Reply-To:     Robin High <robinh@UOREGON.EDU>
Sender:       "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From:         Robin High <robinh@UOREGON.EDU>
Subject:      Re: Fitting and Testing Goodness of Fit to non-Standard Distn in
              SAS
Comments: To: OR Stats <stats112@GMAIL.COM>
In-Reply-To:  A<6eca73440801241234jec7b114ieb195428bdd3075d@mail.gmail.com>
Content-Type: text/plain; charset="us-ascii"

What distributions did you have in mind? If you can compute the CDF
(i.e., with one of SAS's cdf functions or approximate the integral
numerically or some other formula), you can compute GOF with a few SORTs
and DATA steps.

Robin High
University of Oregon


-----Original Message-----
From: SAS(r) Discussion [mailto:SAS-L@LISTSERV.UGA.EDU] On Behalf Of OR
Stats
Sent: Thursday, January 24, 2008 12:34 PM
To: SAS-L@LISTSERV.UGA.EDU
Subject: Fitting and Testing Goodness of Fit to non-Standard Distn in
SAS

Dear All:

I would like to fit and test the goodness of fit of my empirical data to
distributions.  Unfortunately, these two distn's are not pre-coded in
SAS.
So while PROC UNIVARIATE has nice features for fitting and testing the
fit
of our data, it appears to work only if we are interested in fitting our
data to Beta, Exponential, Gamma, Lognormal, Normal, or Weibull.  What
is
the most efficient way of fitting and testing goodness of fit of our
data if
the distn that we assume is not one of these?  Is Proc Model the best
way to
do this in SAS?

While one of my distn's has closed form, the density of my other
assumption
does not.  So if I would like to fit my data to a non-closed form
density
function, and again test for goodness of fit, how can I best do this in
SAS?

Thank so much,
ORstats