Correcting the Minimization Bias in Searches for Small Signals

Abstract

We discuss a method for correcting the bias in the limits for small signals if those limits were found based on cuts that were chosen by minimizing a criterion such as sensitivity. Such a bias is commonly present when a "minimization" and an "evaluation" are done at the same time. We propose to use a variant of the bootstrap to adjust the limits. A Monte Carlo study shows that these new limits have correct coverage.

14 pages, 5 figues

References in corpus (3)

• A Unified Approach to the Classical Statistical Analysis of Small Signals
• Confidence Intervals and Upper Bounds for Small Signals in the Presence of Background Noise
• Understanding stochastic perturbation theory: toy models and statistical analysis

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