ENBIS: European Network for Business and Industrial Statistics
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ENBIS7 in Dortmund
24 – 26 September 2007The following abstracts have been accepted for this event:

Use of Experimental Design to analyze a Neck Forming Process
Authors: J. Kunert , E. Tekkaya , L. Kwiatkowski , O. Melsheimer and S. Straatmann (University of Dortmund, Dortmund, Germany)
Primary area of focus / application:
Submitted at 24Jun2007 20:20 by
Accepted
The approach we chose starts with the identification of signifant influencing variables using a fractional factorial experiment. For process optimization and robustification we apply methods which have already proved their usefulness for another incremental forming process, namely sheet metal spinning.
In this talk, we present the methodology with the help of an example. In this example we try to neckform straight bead welded steel pipes in a inner range to achieve one given geometry. 
Improving alarm systems by classification
Authors: Wiebke Sieben (University of Dortmund, Dortmund, Germany)
Primary area of focus / application:
Submitted at 25Jun2007 10:08 by
Accepted
intensive care. In situations were classical process control methods
cannot be applied, existing alarm systems can be improved by
classification procedures. This is the case when no "in control state"
exists or when the process to be monitored is high dimensional, complex
and possibly autocorrelated. Annotations to the existing alarm system
that contain an expert's opinion whether an alarm is considered as
"true" or "false" can be used as input for data driven alarm rule
generation. We study the use of ensembles of decision trees as
classifiers for this problem and at the same time take the unequally
severe consequences of misclassifying true as false alarms and false as
true alarms into account. A procedure based on the analogy of this
classification problem to statistical testing is presented and applied
to real data.
The data comes from a standard monitoring system at an intensive care
unit. So far, the alarms, mostly based on univariate signals, are
triggered when a physiological variable crosses a preset threshold.
These standard monitoring systems are known to produce a high number of
false alarms that distract and annoy the care givers. With our new
procedure, the expected sensitivity of the resulting alarm system can be
adjusted to the monitoring environment. This is demonstrated for
sensitivities of 95 percent and 98 percent for which a false alarm
reduction by 46% and 30% is achieved on average. 
The ENBIS papers database
Authors: Christopher McCollin (Nottingham Trent University, Nottingham, UK)
Primary area of focus / application:
Submitted at 25Jun2007 10:09 by
Accepted
content, etc were put together into one spreadsheet on Excel to derive some
preliminary results on final takeup on presentation of papers, main authors,
main subject headings, etc. These results will be presented with details of the
present state of the database and scope for future work. 
Six Sigma, the good, the bad and the very bad
Authors: Jonathan SmythRenshaw
Primary area of focus / application:
Submitted at 28Jun2007 13:13 by
Accepted

Analysis of Repeated Measures Data that are Autocorrelated at Lag(k)
Authors: Serpil Aktas, Melike Kaya (Hacettepe University, Ankara, Turkey)
Primary area of focus / application:
Submitted at 28Jun2007 13:34 by Serpil Aktas
Accepted
analysis. The subjects are assumed to be drawn as a random sample from a homogeneous
population and observations of a variable which are repeated, usually over time.
When data are taken in sequence , such data tend to be serially correlated that is,
current measurements are correlated with past measurements. Withinsubject
measurements are likely to be correlated, whereas betweensubject measurements are
likely to be independent in repeated measures design.
Suppose that Y1, Y2 ,.,Yt are random variables taken from t successive time points.
The Serial dependency can occur between Yt and Yt1 . The corresponding correlation
coefficients are called autocorrelation coefficients. The distance between the
observations that are so correlated is referred as the lag. The covariance structure
of repeated measures involves both the between subject and within subject. Usually,
the between subject errors are assumed independent and the within subject error
assumed correlated. After performing the analysis of variance when there is a
significant differences between the factors, multiple comparisons tests are used. In
these procedures the standard error of the mean is estimated by dividing the
MSwithin from the entire Anova by the number of observations in the group, then
taking the square root of that quantity but the standard error of the mean needs an
autocorrelation correction when the data are autocorrelated. In this study, a
simulation study were performed to illustrate the behavior of the post hoc
procedures when data is lag(k) autocorrelated and results were compared to the usual
procedures. 
Robust elimination of atypical data points in small samples and high dimensions
Authors: Florian Sobieczky, Birgit Sponer and Gerhard Rappitsch
Primary area of focus / application:
Submitted at 6Jul2007 16:46 by Gerhard Rappitsch
Accepted
In particular, we demonstrate the improvement in the case of correlation estimation for various multivariate distributions. For this application,
special attention has to be paid to the influence of atypical
observations
on the geometry of the estimated contour lines of the underlying
density.
Further applications are shown from semiconductor industry to
investigate
the correlation of electrically measured performance parameters after
fabrication (e.g. threshold voltage) and inline measurements of process
parameters (e.g. oxide thickness).
D. L. Donoho, M. Gasko: `Breakdown properties of location estimates based on halfspace depth and projected outlyingness’, Annals of Statistics 1992, Vol. 20, No.4, p. 18031827
P. J. Rousseeuw, A. Struyf: `Computing location depth and regression depth in higher dimensions’, Statistics and Computing, 8:p.193203, 1998. 12
Y. Zuo: `Multidimensional trimming based on projection depth’, Annals of Statistics 2006, Vol.34, No.5,p. 22112251