Binning a set of Continuous Variables using Percentiles for WOE Transformation & Logistic Regression

[JonDickens1607]

I have a SAS Programming Problem that you may have already solved:

My Data Set contains three sets of continuous variables: 

DQ01 - DQ59  DE01 - DE59  &  DL01 - DL59. 

( 177 variables ) Each standardised with Mean = 50 and Variance  = 100

The basic Statistical problem is Binary Logistic Regression.

1. I want to bin each continuous variable using deciles or semi-deciles 

    that have been computed using PROC Univariate / Summary.

2. Compute and output the Percentiles for each Variable.

3. For each variable compare the observed values with the Percentile 

    Cut-Points and then allocate that observation to a Decile Bin.

4. Optimise the Bin Allocation based on a metric such as the GINI.

5. Apply a Robust WOE Transformation to each Binned Variable.

   subject to the following constraints:

   a. The % frequency within each bin > 5%

   b. The WOE transformation is Monotonic 

6. Fit a Binary Logistic Regression Model to the WOE-Transformed Variables.

If you have any advice or suggestions w.r.t. the above please let me know.

Regards

[JBerry]

I think this is a little too big for a forum post. Also you posted it twice in 2 different forums.

[JonDickens1607]

My main problem was how to process a large number of variables using the same binning algorithm.

I have constructed a solution for the binning process for a single variable using proc rank.

Now I need a maco possibly using arrays that enables me to repeat the process and combine the output into a table.

Has this reduced the problem sufficiently?