proc reliability 可以拟合,当然你自己可以写宏程序也是可以的

可以用proc capability 来实现拟合分布和拟合优度的检验，从而挑选出最优分布

%macro red;
%let var_red=red1 red2 red3 ;
%let i=1;
%let var=%scan(&var_red.,&i.);
%do %while(&var. ne %quote());
  proc capability data=ly.doubleball freq robustscale specialindices;
  var &var.;
  title 'Distribution of doubleball &var.';
  histogram &var./ midpoints=1 to 10 by 1  normal exponential lognormal  weibull gamma ;
  run;

%let i=%eval(&i.+1);
%let var=%scan(&var_red.,&i.);
%end;
%mend red;
%red;

The following DATA step statements simulate a sample from a lognormal distribution with population parameters $\mu = 1.5$ and $\sigma =0.25$, and store the sample in the variable Y of a data set Work.Test_sev1:

/*------------- Simple Lognormal Example -------------*/
data test_sev1(keep=y label='Simple Lognormal Sample');
   call streaminit(45678);
   label y='Response Variable';
   Mu = 1.5;
   Sigma = 0.25;
   do n = 1 to 100;
      y = exp(Mu) * rand('LOGNORMAL')**Sigma;
      output;
   end;
run;
The following statements fit all the predefined distribution models to the values of Y and identify the best distribution according to the corrected Akaike’s information criterion (AICC):

proc hpseverity data=test_sev1 crit=aicc;
   loss y;
   dist _predefined_;
run;
The PROC HPSEVERITY statement specifies the input data set along with the model selection criterion, the LOSS statement specifies the variable to be modeled, and the DIST statement with the _PREDEFINED_ keyword specifies that all the predefined distribution models be fitted.