Estimating a signal in the presence of an unknown background

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 nonparametric kernel density estimator. A transformation is used to avoid a problem at the data boundaries. 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.