Handling uncertainties in background shapes: the discrete profiling method

• Main Authors: Dauncey, P. D, Kenzie, M, Wardle, N, Davies, G. J
• Format: Journal Article
• Language: English
• Published: 28-04-2015
• Subjects: Physics - Data Analysis, Statistics and Probability; Physics - High Energy Physics - Experiment
• DOI: 10.48550/arxiv.1408.6865

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.