ENBIS: European Network for Business and Industrial Statistics
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ENBIS15 in Prague
6 – 10 September 2015; Prague, Czech Republic Abstract submission: 1 February – 3 July 2015The following abstracts have been accepted for this event:

Skeletons, Flying Carpets, and a Saw
Authors: Christian Ritter (Université Catholique de Louvain)
Primary area of focus / application: Modelling
Secondary area of focus / application: Design and analysis of experiments
Keywords: Regression, Experimental design, Model averaging, Statistical graphics
Submitted at 2Apr2015 08:07 by Christian Ritter
Accepted
experiments (or theoretical considerations) when there are multiple
inputs, multiple responses, and when the relationships are complex?
Multiplying or combining graphs of single responses with respect to one
or several inputs quickly becomes confusing or oversimplistic.
Animations of such response profile displays and interaction graphs by
sliders can help, but only to a limited extend. The main problem is,
that they don't create visual 'stories' which relate to the subject area
and thereby fail to inspire the scientist. Here we shall describe two
types of dual response displays which can under some conditions overcome
this problem. We then use them to explore data from a paper helicopter
experiment reported by Box and Liu (1999). Here we saw the design into
slices and bones and display the results for a range of weighted
averages between a base and a full model until we understand what's
going on. 
What is a MOOC?
Authors: Nathalie VillaVialaneix (INRA, UR 875 MIAT)
Primary area of focus / application: Education & Thinking
Keywords: MOOC, Online course, Teaching, Statistics, Lifelong learning

Optimization of Stochastic Simulators by Gaussian Process Metamodelling – Application to Maintenance Investments Planning Problems
Authors: Bertrand Iooss (EDF R&D), Thomas Browne (EDF R&D), Loïc Le Gratiet (EDF R&D), Jérome Lonchampt (EDF R&D)
Primary area of focus / application: Design and analysis of experiments
Secondary area of focus / application: Modelling
Keywords: Asset management, Stochastic simulator, Computer experiments, Gaussian process, Uncertainty, Adaptive design, Optimization
During the optimization process, one of the main issues is the computational cost of the stochastic simulator to optimize, which leads to methods requiring minimal simulator runs. The solution investigated in this study is to develop and use a metamodel instead of the simulator within the mathematical optimization algorithm. From a first set of simulator runs (called the learning sample and coming from a specific design of experiments), a metamodel consists in approximating the simulator outputs by a mathematical model. This metamodel can then predict the simulator outputs for other input configurations. Many metamodeling techniques are available in the computer experiments literature. However, these conventional methods are not suitable in the present framework because of the stochastic nature of the simulator: the output of interest is not a single scalar variable but a full probability density function (or a cumulative distribution function, or a quantile function).
We first propose to build a metamodel of the stochastic simulator using the following key points:
1) Emulation of the quantile function which proves better efficiency than the emulation of the probability density function;
2) Decomposition of the quantile function in a sum of the quantile functions coming from the learning sample outputs;
3) Selection of the most representative quantile functions of this decomposition using an adaptive choice algorithm (called the modified magic points algorithm) in order to have a small number of terms in the decomposition;
4) Emulation of each coefficient of this decomposition by a Gaussian process metamodel.
The metamodel is then used to treat a simple optimization strategy maintenance problem using the VME code, in order to optimize a NPV quantile. Using the Gaussian process metamodel framework, an adaptive design method can be defined by extending in our case the well known EGO (Efficient Global Optimization) algorithm. This allows to obtain an “optimal” solution using a small number of VME simulator runs. 
Data Mining in Direct Marketing  Attribute Construction and Decision Tree Induction
Authors: Petra Perner (Institut for Computer Vision and Applied Computer Sciences), Andrea AhlemeyerStubbe (draftcom)
Primary area of focus / application: Mining
Secondary area of focus / application: Business
Keywords: Direct marketing, Data analysis, Decision tree induction, Mailing action
Submitted at 10Apr2015 10:15 by Petra Perner
Accepted
are identified using data mining. Therefore, it is always better to employ data mining methods rather than just mailing all known addresses. 
An Approach for Monitoring Count Data Using Principal Components RegressionBased Control Charts
Authors: Danilo Marcondes Filho (Federal University of Rio Grande do Sul), Ângelo Márcio Oliveira Sant’Anna (Federal University of Bahia)
Primary area of focus / application: Process
Secondary area of focus / application: Modelling
Keywords: Poisson model, Modelbased control chart, PCA, PCA regression, PCAbased control chart.
Submitted at 14Apr2015 22:48 by Danilo Marcondes Filho
Accepted

Early Detection of Long Term Evaluation Criteria in Online Controlled Experiments
Authors: Prof David Steinberg (Tel Aviv University), Yoni Schamroth (Perion Networks), Boris Rabinovich (Perion Networks), Liron Gat Kahalon (Perion Network)
Primary area of focus / application: Design and analysis of experiments
Secondary area of focus / application: Modelling
Keywords: GLM, Resampling statistics, Lifetime value, Controlled experiments
Submitted at 15Apr2015 11:31 by Yoni Schamroth
Accepted