This paper presents several alternative statistical approaches which are based on much weaker hypotheses than the Gaussian one, which result from general probabilistic inequalities and order-statistic based formulas. | Probabilistic risk bounds for the characterization of radiological contamination EPJ Nuclear Sci. Technol. 3 23 2017 Nuclear Sciences G. Blatman et al. published by EDP Sciences 2017 amp Technologies DOI epjn 2017017 Available online at http REGULAR ARTICLE Probabilistic risk bounds for the characterization of radiological contamination Géraud Blatman1 Thibault Delage2 Bertrand Iooss2 and Nadia Pérot3 1 EDF Lab Les Renardières Materials and Mechanics of Components Department 77818 Moret-sur-Loing France 2 EDF Lab Chatou Department of Performance Industrial Risk Monitoring for Maintenance and Operations 78401 Chatou France 3 CEA Nuclear Energy Division Centre de Cadarache 13108 Saint-Paul-lès-Durance France Received 9 December 2016 Received in final form 26 May 2017 Accepted 19 June 2017 Abstract. The radiological characterization of contaminated elements walls grounds objects from nuclear facilities often suffers from too few measurements. In order to determine risk prediction bounds on the level of contamination some classic statistical methods may therefore be unsuitable as they rely upon strong assumptions . that the underlying distribution is Gaussian which cannot be verified. Considering that a set of measurements or their average value come from a Gaussian distribution can sometimes lead to erroneous conclusions possibly not sufficiently conservative. This paper presents several alternative statistical approaches which are based on much weaker hypotheses than the Gaussian one which result from general probabilistic inequalities and order-statistic based formulas. Given a data sample these inequalities make it possible to derive prediction intervals for a random variable which can be directly interpreted as probabilistic risk bounds. For the sake of validation they are first applied to simulated data generated from several known theoretical distributions. Then the proposed methods are applied to two data sets obtained from real .
