Including systematic uncertainties in confidence interval construction for Poisson statistics
Citations Over TimeTop 1% of 2003 papers
Abstract
One way to incorporate systematic uncertainties into the calculation of confidence intervals is by integrating over probability density functions parametrizing the uncertainties. In this paper we present a development of this method which takes into account uncertainties in the prediction of background processes and uncertainties in the signal detection efficiency and background efficiency, and allows for a correlation between the signal and background detection efficiencies. We implement this method with the likelihood ratio (usually denoted as the Feldman-Cousins) approach with and without conditioning. We present studies of coverage for the likelihood ratio and Neyman ordering schemes. In particular, we present two different types of coverage tests for the case where systematic uncertainties are included. To illustrate the method we show the relative effect of including systematic uncertainties in the case of the dark matter search as performed by modern neutrino telescopes.
Related Papers
- Asymptotic confidence interval for the coefficient of variation of Poisson distribution: a simulation study(2010)
- → COMPARISON OF FIVE EXACT CONFIDENCE INTERVALS FOR THE BINOMIAL PROPORTION(2014)6 cited
- → Chapter 50: Evaluating Confidence Intervals: Length and Coverage Probability(2019)1 cited
- → New bootstrap confidence intervals for means of positively skewed distributions(2016)1 cited
- → A method to reduce the width of confidence intervals by using a normal scores transformation(2023)