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 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. 
Wolfgang A. Rolke and Angel M. L\'opez
physics.data-an