ENBIS: European Network for Business and Industrial Statistics
Forgotten your password?
Not yet a member? Please register
ENBIS18 in Nancy
2 – 25 September 2018; Ecoles des Mines, Nancy (France) Abstract submission: 20 December 2017 – 4 June 2018The following abstracts have been accepted for this event:

Designs for Characterization of Rare Events of Monotone Models
Authors: Rodrigo Cabral Farias (I3S CNRS/UNS), Elyes Ouerghi (I3S CNRS/UNS), Luc Pronzato (I3S CNRS/UNS), Maria Joao Rendas (I3S CNRS/UNS)
Primary area of focus / application: Design and analysis of experiments
Keywords: Exceedance probability, Quantile estimation, Monotone functions, Experimental design
Submitted at 27Apr2018 14:57 by Maria Joao Rendas
Accepted
In some circumstances, even if the function is not exactly known, qualitative knowledge about its dependency on the input factors exists. In this presentation we exploit knowledge that f(.) is monotone in its arguments when addressing the following two related problems: (i) to determine the probability α that f(.) exceeds a given level η when upper and lower bounds for α are known; (ii) to find the input configurations and threshold η that map to the α−quantile of f(.).
Monotonicity assumptions have been explored before, in a similar context, in [3]. Our work differs in that we show that by exploiting knowledge of the probability distribution of the input factors of f(.) and about the possible range for α we can restrict the required (costly) evaluations of the function to a small subset of the entire input space. This is not only important from the point of view of reducing the (large) numerical complexity of stateoftheart methods based on surrogate models, but it also (and this is probably equally important) increases overall robustness to the stationarity assumption that underlies these techniques. We discuss subsequent application of the adaptive method presented in [1] to the identified relevant subset, in particular concerning the choice of an initial design.
The results are illustrated numerically on the estimation of the probability of flooding using an hydrodynamic model, enabling an appreciation of how it impacts overall efficiency and accuracy.
Bibliography
[1] Julien Bect, Ling Li, Emmanuel Vazquez. Bayesian subset simulation. SIAM/ASA Journal on Uncertainty Quantification, ASA, American Statistical Association, 2017, 5 (1), pp.762786.
[2] C. Chevalier, J. Bect, D. Ginsbourger, E. Vazquez, V. Picheny,Y. Richet, Fast parallel krigingbased stepwise uncertainty reduction with application to the identification of an excursion set. Technometrics, 56, No.4, pp: 455—465, 2014.
[3] T. LabopinRichard, V. Picheny, "Sequential design of experiments for estimating quantiles of blackbox functions", Statistica Sinica, 2017. 
How SPM Can Help Practitioner when Her/His Process Is Unstable
Authors: Vladimir Shper (Moscow Institute of Steel & Alloys), Yuri Adler (Moscow Institute of Steel & Alloys), Irina Zubkova (Moscow Institute of Steel & Alloys)
Primary area of focus / application: Process
Secondary area of focus / application: Consulting
Keywords: SPM, Control, Charts, Unstable, processes
Submitted at 27Apr2018 15:10 by Vladimir Shper
Accepted

Functional Regression Control Chart for Ship CO2 Emission Monitoring
Authors: Fabio Centofanti (University of Naples Federico II), Antonio Lepore (University of Naples Federico II), Alessandra Menafoglio (MOX  Politecnico di Milano), Biagio Palumbo (University of Naples Federico II), Simone Vantini (MOX  Politecnico di Milano)
Primary area of focus / application: Process
Secondary area of focus / application: Modelling
Keywords: Functional data analysis, Functional linear regression, Profile monitoring, Statistical Process Control, CO₂ emission monitoring
Submitted at 27Apr2018 15:13 by Antonio Lepore
Accepted
In this context, profile monitoring methods can be formulated in order to use the additional information from these covariates as well as that in the quality characteristic.
To this end, we propose a new functional control chart that combines information from all the measures attainable, with the aim of improving the efficacy of the monitoring.
The rationale behind the proposed control chart is to monitor the residuals of a functiononfunction linear regression of the quality characteristic on the covariates.
This allows monitoring the quality characteristic adjusted for the effect of the covariates and, thus, taking into account their explained variance.
A Monte Carlo simulation study is presented to quantify the performance of the proposed control chart in identifying quality characteristic mean shift, through a comparison with other functional control charts proposed in the literature.
A real case study in the shipping industry is finally presented, with the purpose of monitoring CO₂ emissions from a RoPax ship owned by the shipping company Grimaldi Group and in particular detecting the CO₂ emission reductions after that a specific energy efficiency initiative (EII) was performed. 
Comparison of Methods for Summarizing and Simulating NonStandard Distributions
Authors: Jody Muelaner (The University of Bath), Kavya Jagan (The National Physical Laboratory)
Primary area of focus / application: Metrology & measurement systems analysis
Secondary area of focus / application: Quality
Keywords: Monte Carlo, Pearson distribution, Johnson distribution, Nonstandard distributions, Uncertainty of measurement
Submitted at 27Apr2018 23:00 by Jody Muelaner
Accepted

KrigingBased Robust Optimization
Authors: Celine Helbert (Ecole Centrale de Lyon), Melina Ribaud (Ecole Centrale de Lyon), Christophette Blanchet (Ecole Centrale de Lyon), Frederic Gillot (Ecole Centrale de Lyon)
Primary area of focus / application: Quality
Secondary area of focus / application: Design and analysis of experiments
Keywords: Robust optimization, Kriging, Expected improvment, Genetic algorithm, Robustness criterion

Experimental Designs in Industrial Marketing
Authors: Paulin Choisy (XLstat), Efthalia Anagnostou (XLstat)
Primary area of focus / application: Other: Software
Keywords: Conjoint analysis, Experimental design, Software, Optimal design