ENBIS: European Network for Business and Industrial Statistics
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ENBIS12 in Ljubljana
9 – 13 September 2012 Abstract submission: 15 January – 10 May 2012The following abstracts have been accepted for this event:

Splitplot Design and Mixed Response Surface Models
Authors: Rossella Berni (University of Florence)
Primary area of focus / application: Design and analysis of experiments
Keywords: Splitplot design, Robust design, Mixed Response surface models, Variance components

Challenges in Using Mixture DoE to Understand the Complex Phase Behaviour of a 3Component Formulation
Authors: Phil Kay (Fujifilm Imaging Colorants Ltd.)
Primary area of focus / application: Design and analysis of experiments
Keywords: Mixture, Formulation, Phase diagram, Visualisation
Various modelling approaches (response transformation, logistic regression, neural network) were tried but ultimately it proved impossible to build a useful predictive model from an economical set of design points. Nevertheless, I will show that the DoE method was instrumental in us achieving our stated aim with much improved efficiency over the traditional approach. I will also discuss whether other approaches would be more efficient for arriving at phasebehaviour information. 
Evaluation of Measurement Uncertainty and Regulatory Context: An Application in Fire Engineering
Authors: Alexandre Allard (Laboratoire National de Métrologie et d'Essais), Nicolas Fischer (Laboratoire National de Métrologie et d'Essais), Franck Didieux (Laboratoire National de Métrologie et d'Essais), Eric Guillaume (Laboratoire National de Métrologie et d'Essais)
Primary area of focus / application: Reliability
Keywords: Fire Safety, Computational Code, Sensitivity Analysis, Measurement uncertainty, Probability of exceeding a threshold
As a consequence, these quantities require to be investigated. In the first place, their uncertainty is considered in terms of central dispersion but also of their behaviour for the extreme values of the probability distribution. In the second place, the most influent input quantities to explain their variability are pointed out through a sensitivity analysis. Different techniques are used for both objectives such as the Monte Carlo simulation to estimate their probability distribution and other parameters such as the mean, the standard deviation or the probability of exceeding a threshold. Different methods for sensitivity analysis are also discussed regarding the computational cost. The local polynomial estimation succeeded in providing a suitable sensitivity evaluation and highlighted the most influent input quantities. 
A Poisson Gamma Hierarchical Model for Estimating the Complication Rates of Bladder Cancer
Authors: Özge Karadağ (Hacettepe University), Gül Ergü (Hacettepe University)
Primary area of focus / application: Modelling
Keywords: Poisson – gamma hierarchical model, Hierarchical Bayes, Hyperparameter estimation, Gibbs sampling
Submitted at 13Apr2012 14:45 by Özge Karadağ
Accepted
In this study a hierarchical model structure and a Bayesian procedure are considered to reach more accurate estimations. A Bayesian Poissongamma hierarchical model is built to estimate the individual complication rates for bladder cancer. 
How to Choose a Fragility Curve? Bayesian Decision Theory Applied to Uncertainty Analysis in an Industrial Context
Authors: Guillaume Damblin (AgroParisTech / EDF R&D), Merlin Keller (EDF R&D), Alberto Pasanisi (EDF R&D), Irmela Zentner (EDF R&D), Pierre Barbillon (AgroParis Tech), Eric Parent (AgroParis Tech)
Primary area of focus / application: Reliability
Keywords: Reliability, Engineering  Industry, Bayesian decision theory, seismic fragility curve
We propose a novel approach to estimate the fragility curve, described by a parameterized form, based on data sets from both computer and physical experiments. We adopt a Bayesian framework, that is, we propose to take into account explicitly, by a probability distribution, the uncertainty about the fragility curve. This enables us to benefit from both expert information and available data, hence reducing the uncertainty about the curve.
More importantly, by using Bayesian decision theory, it is then possible to build an estimator of the fragility curve that takes into account the consequences of over and underestimation of the probability of failure. To this aim, we propose cost functions measuring the gap between the unknown curve and its estimate, whose qualities are described in terms of the consequences of its use in engineering. The benefits of this approach is illustrated using results of experiments on both simulated and real datasets, where we demonstrate how to choose a fragility curve tailored to the stakes motivating its estimation. 
A Bayesian Approach for Inference in POD Models
Authors: Merlin Keller (EDF R&D), Nicolas Bousquet (EDF R&D)
Primary area of focus / application: Reliability
Keywords: Reliability, Engineering  Industry, Baysian inference, probability of detection, nondestructive experiments
Submitted at 13Apr2012 15:49 by Merlin Keller
Accepted
of industrial equipments/components of electric power plants, the flaw size is often a key variable whose distribution has to be carefully estimated.
The assessment of this distribution is usually not trivial. Indeed, the data available to this purpose typically come from nondestructive experiments (NDE), affected by observational noise and progressive censoring due to the detection limits of the testing process. This censoring is characterized by the probability of detection (POD) function, that is, the probability of detecting a flaw conditionally on its size, whose exact value is generally uncertain.
In this paper, we show how a combination of these data with observations coming from destructive experiments allows to estimate both the flaw size distribution and the probability of detection (POD) function. Though maximum likelihood techniques can be used to this end, they may be inappropriate given the wide uncertainty about the POD function, and the difficulty of constructing valid confidence intervals.
Instead, we propose to use a Bayesian approach to derive a posterior distribution for the POD function, based on both expert information and available data. A point estimate can then be derived by minimizing the posterior expectation of a cost function. This allows to penalize differently under and overestimation of the failure probability, and yields conservative estimates for both the POD function and the flaw size distribution. We demonstrate the benefits of this approach on both simulated and real flaw size data sets.