#
The ENTROPY Procedure

The ENTROPY procedure implements a parametric method of linear estimation based on
generalized maximum entropy. The ENTROPY procedure is suitable when there are outliers
in the data and robustness is required, when the model is ill-posed or underdetermined
for the observed data, or for regressions that involve small data sets.

The main features of the ENTROPY procedure are as follows:

- estimation of simultaneous systems of linear regression models
- estimation of Markov models
- estimation of seemingly unrelated regression models
- estimation of unordered multinomial discrete choice models
- solution of pure inverse problems
- allowance of bounds and restrictions on parameters
- performance of tests on parameters
- allowance of data and moment constrained generalized cross entropy

## Documentation

For further details, see the *SAS/ETS*^{®} User's Guide

## Examples