A Novel Universal Statistic for Computing Upper Limits in Ill-behaved Background

Understand

Analysis of experimental data must sometimes deal with abrupt changes in the distribution of measured values.

• Setting upper limits on signals usually involves a veto procedure that excludes data not described by an assumed statistical model.
• We show how to implement statistical estimates of physical quantities (such as upper limits) that are valid without assuming a particular family of statistical distributions, while still providing close to optimal values when the data is from an expected distribution (such as Gaussian or exponential).
• This new technique can compute statistically sound results in the presence of severe non-Gaussian noise, relaxes assumptions on distribution stationarity and is especially useful in automated analysis of large datasets, where computational speed is important.