ENBIS: European Network for Business and Industrial Statistics
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ENBIS8 in Athens
21 – 25 September 2008 Abstract submission: 14 March – 11 August 2008The following abstracts have been accepted for this event:

Comparing different transformation strategies on a sixfactor full factorial (2^6) industrial experiment.
Authors: Erik Mønness
Affiliation: Hedmark University College
Primary area of focus / application:
An empirical power transformation is determined graphically from the σ≈μ^(λ1)relation. Another strategy is derived from the BoxCox transformation.
Issues to be explored:
• What λ is best based on the saturated model?
• Is the estimate stable considering different fractions of the data?
Models involving the experimental factors:
• Applying different models, e.g. involving only main effects, 2interactions, 3interactions, how stable is the λ estimate?
• How does the transformation affect the set of significant factors?
Since one motivation for doing a transformation is to simplify any model, this issue is of interest when having many experimental factors. 
Cumulative copula charts for controlling the dependence among multivariate observations
Authors: András Zempléni, Pál Rakonczai, Csilla Hajas
Affiliation: Eötvös Loránd University, Budapest
Primary area of focus / application:
We use the popular copulaapproach for this approach. The copula allows for investigation the dependence structure separately from the univariate distributions, which is especially useful if the marginal distributions may change without causing an error, like in our case. Assuming that the standard charts deal with the univariate modeling as well as the changes in the mean vector, with the proposed new chart all aspects of important, possible changes are taken care for.
The fit of the observed copula to the one, considered as nullhypothesis can be measured by the multivariate version of the probability integral transform. Critical values can be constructed by simulation (see Rakonczai and A. Zempléni, 2007 for example). We illustrate the use of the chart by simulation studies and by real life bivariate data from our university, where the dependence between highschool final examination results and average achievements at the university is investigated.
Reference
P. Rakonczai and A. Zempléni, 2007: „ Copulas and goodness of fit tests " in: Recent Advances in Stochastic Modelling and Data Analysis, World Scientific, (Editor: C. Skiadas) 
The use of factor and cluster analysis in marketing research.
Authors: Antigone G. Kyrousi, Athanasios G. Poulis
Primary area of focus / application:
Submitted at 30Apr2008 11:07 by Athanasios Poulis
Accepted
In this paper, a distinction is made between exploratory and confirmatory factor analysis while the existing literature is being reviewed. Recent trends, such as the increasing emphasis placed upon confirmatory rather than exploratory factor analysis, as well as the use of factor analysis along with conjoint analysis, are further identified.
In addition, cluster analysis, which is a set of methodologies, is being identified as the most common tool for classification in the marketing field. However, its problematic use especially in qualitative research has been observed and thus, suggestions have been proposed for the marketing researcher.
Furthermore, some recent applications of factor and cluster analysis in marketing problems are being displayed in order to illustrate the problems at hand. Finally, conclusions are drawn, regarding the misapplications of cluster and factor analysis in marketing research. 
Iterative Designs of experiments for constraint approximation
Authors: Picheny V., Ginsbourger D., Roustant O., Haftka R.T.
Affiliation: Ecole Nationale Superieure des Mines de Saint Etienne / University of Florida
Primary area of focus / application:
Submitted at 30Apr2008 14:10 by Victor Picheny
Accepted
We propose a modification of the classical IMSE criterion, based on an explicit tradeoff between reduction of global uncertainty and exploration of target regions, by using the statistical information given by the Kriging model. Sequential strategies are then used to build optimal designs of experiments.
The method is illustrated on several testproblems of dimensions one, two and six. It is shown that compared to classical spacefilling strategies, the error on target regions can be reduced very significantly, with reasonable payoff on the global accuracy.
Finally, the method is tested on a propagation of uncertainty problem, resulting with a gain in accuracy of several orders of magnitude on the probability of failure compared to spacefilling designs. 
A Methodology for the Detection of Segments of Clients with Homogenous Water Consumption Habits. Application to the City of Barcelona.
Authors: S. Fontdecaba, L. Marco, V. Martinez de Pablo, L. Rodero, J.A. Sanchez,I. Sole, X. TortMartorell, J. Zubelzu
Affiliation: UPC (Universitat Politècnica de Catalunya) and Grupo AGBAR
Primary area of focus / application:
Submitted at 30Apr2008 16:52 by Xavier TortMartorell
Accepted
Prior to having models for predicting water demand, the goal of our approach has been segmenting clients into meaningful groups in order to better understand the water consumption behaviour. The study is based on a database with more than one million observations from Barcelona and surrounding towns.
Cluster analysis and decision trees have been used to obtain a descriptive segmentation of clients. The result is a stable partition in 6 groups. This segmentation is the same with and without considering the actual water consumption as a variable, which entails a different behaviour on water consumption depending on socioeconomical variables. Although the study is based on the case of the Barcelona area, it allows the development of a general methodology, which will be described in the presentation. 
Optimal designs for Gaussian random field regression models
Authors: Maroussa Zagoraiou, Alessandro Baldi Antognini
Affiliation: Department of Statistical Sciences, University of Bologna, Italy
Primary area of focus / application:
In particular we consider the problem of designing experiments (i.e. sampling in time or in the real line) when the observations can be modelled via a Gaussian process with a regressive trend component and an exponential correlation structure.
This modelling approach has been widely used for the analysis of computer experiments
(see for instance Sacks, Welch, Mitchell and Wynn, 1989) and in empirical and theoretical finance, in order to model continuous time interest rates (see Gourieroux and Jasiak, 2008).
Assuming the Maximum Likelihood approach, we study the optimal design problem for the estimation of the unknown parameters of the model using a criterion based on the Fisher information matrix.
Sacks J., Welch W.J., Mitchell T.J. and Wynn H.P. (1989) Design and analysis of computer experiments, Statistical Sciences, 4, 409423.
Gourieroux C. and Jasiak J. (2008) Financial Econometrics: Problems, Models, and Methods. Princeton University Press