Handling uncertainties in background shapes: the discrete profiling method
Data Analysis, Statistics and Probability
2015-05-20 v5 High Energy Physics - Experiment
Abstract
A common problem in data analysis is that the functional form, as well as the parameter values, of the underlying model which should describe a dataset is not known a priori. In these cases some extra uncertainty must be assigned to the extracted parameters of interest due to lack of exact knowledge of the functional form of the model. A method for assigning an appropriate error is presented. The method is based on considering the choice of functional form as a discrete nuisance parameter which is profiled in an analogous way to continuous nuisance parameters. The bias and coverage of this method are shown to be good when applied to a realistic example.
Cite
@article{arxiv.1408.6865,
title = {Handling uncertainties in background shapes: the discrete profiling method},
author = {P. D. Dauncey and M. Kenzie and N. Wardle and G. J. Davies},
journal= {arXiv preprint arXiv:1408.6865},
year = {2015}
}
Comments
Accepted by J.Inst