arXiv subjects

Luc Demortier

Publications and source records attributed to Luc Demortier.

4 recordsLinked to original sources

arXiv2014

Testing Hypotheses in Particle Physics: Plots of $p_{0}$ Versus $p_{1}$

For situations where we are trying to decide which of two hypotheses $H_{0}$ and $H_{1}$ provides a better description of some data, we discuss the usefulness of plots of $p_{0}$ versus $p_{1}$, where $p_{i}$ is the $p$-value for testing $H_{i}$. They provide an interesting way of understanding the difference between the standard way of excluding $H_{1}$ and the $CL_{s}$ approach; the Punzi definition of sensitivity; the relationship between $p$-values and likelihood ratios; and the probability of observing misleading evidence. They also help illustrate the Law of the Iterated Logarithm and the Jeffreys-Lindley paradox.

stat.ME↗

arXiv2010

Reference priors for high energy physics

Bayesian inferences in high energy physics often use uniform prior distributions for parameters about which little or no information is available before data are collected. The resulting posterior distributions are therefore sensitive to the choice of parametrization for the problem and may even be improper if this choice is not carefully considered. Here we describe an extensively tested methodology, known as reference analysis, which allows one to construct parametrization-invariant priors that embody the notion of minimal informativeness in a mathematically well-defined sense. We apply this methodology to general cross section measurements and show that it yields sensible results. A recent measurement of the single top quark cross section illustrates the relevant techniques in a realistic situation.

stat.AP↗

arXiv2004

Interval estimation in the presence of nuisance parameters. 1. Bayesian approach

We address the common problem of calculating intervals in the presence of systematic uncertainties. We aim to investigate several approaches, but here describe just a Bayesian technique for setting upper limits. The particular example we study is that of inferring the rate of a Poisson process when there are uncertainties on the acceptance and the background. Limit calculating software associated with this work is available in the form of C functions.

physics.data-an↗

arXiv2003

Constructing Ensembles of Pseudo-Experiments

The frequentist interpretation of measurement results requires the specification of an ensemble of independent replications of the same experiment. For complex calculations of bias, coverage, significance, etc., this ensemble is often simulated by running Monte Carlo pseudo-experiments. In order to be valid, the latter must obey the Frequentist Principle and the Anticipation Criterion. We formulate these two principles and describe some of their consequences in relation to stopping rules, conditioning, and nuisance parameters. The discussion is illustrated with examples taken from high-energy physics.

physics.data-an↗