ENBIS: European Network for Business and Industrial Statistics
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ENBIS14 in Linz
21 – 25 September 2014; Johannes Kepler University, Linz, Austria Abstract submission: 23 January – 22 June 2014The following abstracts have been accepted for this event:

Constrained OnLine Optimization Using Evolutionary Operation: A Case Study about EnergyOptimal Robot Control
Authors: Koen Rutten (KU Leuven), Josse De Baerdemaekera (KU Leuven), Julian Stoev (Flanders Mechatronics & Technology Centre), Maarten Witters (Flanders Mechatronics & Technology Centre), Bart De Ketelaere (KU Leuven)
Primary area of focus / application: Design and analysis of experiments
Secondary area of focus / application: Process
Keywords: EVOP, Online optimization, Desirability, Steepest ascent, Case study
Submitted at 30Apr2014 15:15 by Koen Rutten
Accepted

An Adapted Screening Concept to Detect Risk Devices
Authors: Anja Zernig (KAI  Kompetenzzentrum für Automobil und Industrieelektronik GmbH), Olivia Bluder (KAI  Kompetenzzentrum für Automobil und Industrieelektronik GmbH), Jürgen Pilz (AlpenAdria Universität Klagenfurt), Andre Kästner (Infineon Technologies Austria AG)
Primary area of focus / application: Reliability
Secondary area of focus / application: Quality
Keywords: Reliability, Screening, Mavericks, ICA, Negentropy
For Maverick detection on technologies with smaller structures it became apparent that the inspection of the measurement data themselves is insufficient. Moreover, an investigation of up to thousand available measurements is counterproductive as it leads to a huge amount of wrongly rejected devices and choosing a valuable subset of measurements depends on available expert knowledge. Remedy provides a data transformation, separating meaningful information from noise in the measurements. We propose to use Independent Component Analysis. Its concept is based on a segregation of additive mixtures (measurements) in independent random variables (sources). As the mixing process is unknown, demixing the measurements is an optimization problem, resulting in an estimation of the independent sources.
Henceforth, sources instead of measurement data are analyzed for conspicuities. A distinction between sources with major and minor contribution to the detection of Mavericks is conducted to further improve the screening method. The measure of Negentropy, which quantifies the deviation of a source from a Gaussian distribution, is applied to identify nonGaussian values in the sources. Those values are accused to be the desired Mavericks. First evaluations provide promising results that this approach leads to a reliable detection of Mavericks with a minor amount of misclassifications. 
Extreme Value Statistics and Robust Filtering for Hydrological Data
Authors: Bernhard Spangl (BOKU  University of Natural Resources and Life Sciences, Vienna), Peter Ruckdeschel (Fraunhofer ITWM, Kaierslautern)
Primary area of focus / application: Modelling
Secondary area of focus / application: Metrology & measurement systems analysis
Keywords: Extreme values, Robust filtering, EM algorithm, Hydrology, River discharge
Submitted at 30Apr2014 22:16 by Bernhard Spangl
Accepted

Signal Interpretation in Hotelling's T2C Control Chart for Compositional Data
Authors: Marina VivesMestres (Universitat de Girona), Josep DaunisiEstadella (Universitat de Girona), JosepAntoni MartínFernández (Universitat de Girona)
Primary area of focus / application: Process
Secondary area of focus / application: Quality
Keywords: Compositional data, Logratio, Hotelling T2, Multivariate control chart, Signal interpretation
Submitted at 1May2014 13:37 by Marina VivesMestres
Accepted
In industry it is common to measure process performance by a set of interrelated continuous variables. In those cases, a widely used charting statistic is the Hotelling’s T2 which takes into account both the univariate and the interrelationship effects between variables. However, multivariate process monitoring may mask the cause of the anomaly because of the dimensionality reduction from a pdimensional vector to a unidimensional statistic. One efficient procedure for interpreting signals in a T2 control chart is by decomposing the T2 statistic into orthogonal components directly interpretable known as MYT decomposition [1].
VivesMestres et al. [2] proposed the T2C control chart suitable for monitoring compositional data (CoDa). Compositions are vectors of positive elements describing quantitatively the parts of some whole, which carry exclusively relative information between the parts. Their sample space is the simplex and specific statistical methods are necessary because of its particular geometry. T2C control chart is based on the logratio methodology and is plenty consistent with the characteristics of CoDa. Broadly speaking, T2C control chart is equivalent to applying the typical methodology to the compositions expressed in coordinates with respect to an orthonormal basis.
The difficulty arises again when the causes of the signal in the T2C control chart have to be identified. VivesMestres et al. [3] dealt with the case of three part compositions (p=3). In this work, we present a general method (p>3) for interpreting such signals based on the MYT decomposition.
References
[1] Mason, R. L., N. D. Tracy, and J. C. Young (1995). Decomposition of T2 for multivariate control chart interpretation. Journal of Quality Technology, 27 (2), 99108.
[2] VivesMestres, M., J. DaunisiEstadella, and J. A. MartínFernández (2014). Individual T2 control chart for compositional data. Journal of quality technology, 46 (2), 127139.
[3] VivesMestres, M., DaunisiEstadella, J., & MartínFernández, J.A. (2014). OutofControl Signals in ThreePart Compositional T2 Control Chart. Quality and Reliability Engineering International, 30(3), 337–346. 
More Precise Treatment Comparisons in User Studies by Modeling Baseline Effects
Authors: JanWillem Bikker (Consultants in Quantitative Methods (CQM))
Primary area of focus / application: Modelling
Secondary area of focus / application: Business
Keywords: User study, Linear mixed model, Structural equation model, Heteroskedasticity, Baseline effect, Visual analogue scale
Submitted at 1May2014 19:18 by JanWillem Bikker
Accepted

Credit Portfolio Models from a Copula Perspective
Authors: Matthias Fischer (Universität ErlangenNürnberg)
Primary area of focus / application: Finance
Keywords: CreditRisk+, CreditMetrics, CreditPortfolioView, CreditValueatRisk
Submitted at 2May2014 22:39 by Matthias Fischer
Accepted
the model risk accompanied by e.g. distributional assumptions. In this talk, we analyze the implicit or explicit copulas of popular credit portfolio models as well as the model risk accompanied by the choice of a certain dependency structure by means of a hypothetical Portfolio. Our analysis incorporates representatives
from the elliptical, the generalized hyperbolic and the Archimedean copula classes together with two implicit copulas, implicitly dened by the portfolio
model.