Davison, A. C. ; Sartori, N.

Accepted in: Statistical Science

Particle physics experiments such as those run in the Large Hadron Collider result in huge quantities of data, which are boiled down to a few numbers from which it is hoped that a signal will be detected. We discuss a simple probability model for this and derive frequentist and non-informative Bayesian procedures for inference about the signal. Both are highly accurate in realistic cases, with the frequentist procedure having the edge for interval estimation, and the Bayesian procedure yielding slightly better point estimates. We also argue that the significance, or P-value, function based on the modified likelihood root, provides a comprehensive presentation of the information in the data and should be used for inference.

Keyword(s): Bayesian inference; , Higher order asymptotics; , Large Hadron Collider; , Likelihood; , Non-informative prior; , Orthogonal parameter; , Particle physics; , Poisson distribution; , Signal detection

Reference: STAT-ARTICLE-2008-005