Entropy minimization for parameter estimation problems with unknown distribution of the output noise

We consider the situation where the parameters /spl theta/ of a linear regression model have to be estimated from observations corrupted by an additive noise with unknown distribution f. Since maximum likelihood estimation cannot be used, we estimate /spl theta/ by minimizing the entropy of a kernel estimate of f, constructed from the residuals. An example of parameter estimation in the presence of interference with random binary signals is presented.