For example, I have a sample loss given default dataset with 317 observations: lgd(real loss given default) is the dependent variable; lgd_a(mean default rate by industry), lev(leverage coefficient by firm) and i_def( mean default rate by year) are independent variables. The kernel distribution is plotted and difficult to be estimated by naked eyes.

```
data lgddata;
informat lgd lev 12.9 lgd_a 6.4 i_def 4.3;
input lgd lev lgd_a i_def;
label lgd = 'Real loss given default'
lev = 'Leverage coefficient by firm'
lgd_a = 'Mean default rate by year'
i_def = 'Mean default rate by industry';
cards;
0.747573451 0.413989786 0.6261 1.415
/* Other data*/
0.748255544 0.607452819 0.3645 3.783
;
run;
proc kde data = lgddata;
univar lgd / plots = all;
run;
data _lgddata01;
set lgddata;
id + 1;
run;
proc transpose data = _lgddata01 out = _lgddata02 ;
by id;
run;
proc sgplot data = _lgddata02;
hbox col1 / category = _LABEL_;
xaxis label = ' ';
run;
```

What I need PROC FMM to do is to estimate: 1. which distribution is the best from beta, lognormal, and gamma distributions; 2. how many components (ranging from 1 to 10) are the best for each distribution. To automate and visualize the process, I designed a macro. From the plots above, all penalized criterions (AIC, BIC, etc.) indicate that beta distribution is better than the other two. Also the beta distribution has higher Pearson statistic value and less parameter numbers. ```
ods html style = money;
%macro modselect(data = , depvar = , kmin= , kmax = , modlist = );
%let modcnt=%eval(%sysfunc(count(%cmpres(&modlist),%str( )))+1);
%do i = 1 %to &modcnt;
%let modelnow = %scan(&modlist, &i);
ods output fitstatistics = &modelnow(rename=(value=&modelnow));
ods select densityplot fitstatistics;
proc fmm data = &data;
model &depvar = / kmin=&kmin kmax= &kmax dist=&modelnow;
run;
%end;
data _final;
%do i = 1 %to &modcnt;
set %scan(&modlist, &i);
%end;
run;
proc sgplot data = _tmp01;
%do i = 1 %to &modcnt;
%let modelnow = %scan(&modlist, &i);
series x = descr y = &modelnow;
where descr ne :'E' and descr ne :'P';
%end;
yaxis label = ' ' grid;
run;
proc transpose data = _tmp01 out = _tmp02;
where descr = :'E' or descr = :'P';
id descr;
run;
proc sgplot data = _tmp02;
bubble x = effective_parameters y = effective_components
size = pearson_statistic / datalabel = _name_;
xaxis grid; yaxis grid;
run;
%mend;
%modselect(data = lgddata, depvar = lgd, kmin= 1,
kmax = 10, modlist = beta lognormal gamma);
```

The optimized component number for the beta distribution is 5 – beautiful matching curve. Lognormal distribution exhausted the maximum 10 components and fits the kernel distribution very awkwardly. Gamma distribution used 9 components and fits relatively well.

Then I chose the 5-compenent Homogeneous beta distribution to model the LGD data. PROC FMM provided all parameter estimates for these 5 components. From the plot above, the intercepts and the scale parameter s are different as expected. Interestingly, the parameters of lgd_a(mean default rate by industry) present big diversity, while the parameters of i_def( mean default rate by year) tend to converge at the zero point.

```
ods output parameterestimates = parmds;
proc fmm data = lgddata;
model lgd = lev lgd_a i_def / k = 5 dist=beta;
run;
proc sgplot data = parmds;
series x = Effect y = Estimate / group = Component;
xaxis grid label = ' '; yaxis grid;
run;
ods html style = htmlbluecml;
```

In conclusion, although PROC FMM is still an experimental procedure, its powerful model selection features would significantly change the way how people feel and use the loss data in the risk management industry.