ENBIS: European Network for Business and Industrial Statistics
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Overview of all Abstracts
The following PDF contains the Abstractbook as it will be handed out at the conference. It is only here for browsing and maybe later reference. All abstracts as PDF
The following abstracts have been accepted for this event:

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

A comparison of neural network and control charting for monitoring profiles in manufacturing processes
Authors: M. Pacella (1) and Q. Semeraro (2)
Primary area of focus / application:
Submitted at 10Jul2007 18:22 by Massimo Pacella
Accepted
The aim of this work is to explore a different approach for monitoring profiles, which uses the Adaptive Resonance Theory (ART) neural network. The implementation of this neural network is based on a set of profiles which are representative of the process in its natural, or incontrol, state.
Throughout the paper, a real case study related to profiles data obtained by a common machining process is used. With reference to the Phase II of profile monitoring, performance of the proposed approach are compared to those of multivariate control charting of the parameters vector. Although the proposed neural network does not produce always outperforming results, it presents comparable performance in several cases. The main advantage presented by the approach is that the model of profile data is “autonomously” derived by the neural network, without requiring any further intervention by the quality practitioner. This feature may create an important bridge between profile monitoring and quality monitoring of several specifications in actual applications.
Affiliations:
(1) Università del Salento, Dipartimento di Ingegneria dell'Innovazione, Lecce, Italy
(2) Politecnico di Milano, Dipartimento di Meccanica, Milano, Italy

Software reliability growth models: systematic descriptions and implementations
Authors: Ed Brandt, Isaac Corro Ramos (corresponding author), Alessandro Di Bucchianico and Rob Henzen
Primary area of focus / application:
Submitted at 30Jul2007 15:01 by Isaac Corro Ramos
Accepted
We also report on the status of a new tool that we are developing to support our systematic approach. Existing tools for software reliability analysis like Casre and Smerfs3 do not make full use of stateoftheart statistical methodology or do not conform to best practices in statistics. Our tool uses welldocumented stateoftheart algorithms and encourages applying best practices from statistics. Moreover, it can easily be extended to incorporate new models. We decided to use Java for the interface (platform independent) and the statistical programming language R (see www.rproject.org ) for the statistical computations. We pay special attention to convergence issues and apply specific algorithms that avoid standard numerical problems.

Analysis of CUSUM and EWMA control charts for Poisson data under parameter estimation
Authors: Murat Caner Testik
Primary area of focus / application:
Submitted at 9Aug2007 09:28 by
Accepted
Key words: Attributes control chart, CUSUM, EWMA, Poisson distribution, markov chain, statistical process control, estimated parameters.

Analysis of CUSUM and EWMA control charts for Poisson data under parameter estimation
Authors: Murat Caner Testik (Hacettepe University, Ankara, Turkey)
Primary area of focus / application:
Submitted at 9Aug2007 09:31 by
Accepted
Although the Poisson distribution may be an appropriate model for such type of processes, incontrol process parameters may be unknown in practice and these may be replaced with the estimates from a reference sample. Due to the additional variability introduced by parameter estimation, operational performance of a control chart might differ from the expected performance when the parameters are known.
In this research, effect of estimated process mean on the performance of the CUSUM and EWMA type control charts are discussed for Poisson data monitoring.
Key words: Attributes control chart, CUSUM, Poisson distribution, markov chain, statistical process control, estimated parameters.
Specifics: Submitted for consideration as a talk (not a poster presentation). 
Testing randomness for the gaming industry: tackling the multiple testing issue
Authors: Dr Neil H. Spencer, University of Hertfordshire, U.K.
Primary area of focus / application:
Submitted at 16Aug2007 09:22 by
Accepted
PLEASE NOTE: I WILL NOT BE ABLE TO ARRIVE IN DORTMUND BEFORE LUNCHTIME ON MONDAY 24TH SEPTEMBER, SO I WOULD BE OBLIGED IF THIS WAS TAKEN INTO ACCOUNT WHEN SCHEDULING MY TALK (IF ACCEPTED). MANY THANKS IF THIS IS POSSIBLE. NEIL SPENCER. 
Data mining of a mail order customer database for Kansei Engineering
Authors: Kathryn Smith and Shirley Coleman (University of Newcastle upon Tyne, Newcastle upon Tyne, UK)
Primary area of focus / application:
Submitted at 22Aug2007 13:31 by
Accepted