Title of article :
Estimating a signal in the presence of an unknown background
Author/Authors :
Rolke، نويسنده , , Wolfgang A. and Lَpez، نويسنده , , Angel M.، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2012
Abstract :
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.
Keywords :
Bootstrap , Maximum likelihood , coverage , Monte Carlo , Transformations
Journal title :
Nuclear Instruments and Methods in Physics Research Section A
Journal title :
Nuclear Instruments and Methods in Physics Research Section A