  We describe a method for fitting distributions to data which only requires
knowledge of the parametric form of either the signal or the background but not
both. The unknown distribution is fit using a non-parametric kernel density
estimator. The method returns parameter estimates as well as errors on those
estimates. Simulation studies show that these estimates are unbiased and that
the errors are correct.
