ENBIS-7 in Dortmund

24 – 26 September 2007

My abstracts


The following abstracts have been accepted for this event:

  • The use of the bootstrap in the analysis of the 12-run Plackett-Burman design.

    Authors: Anthony Cossari
    Primary area of focus / application:
    Submitted at 3-Sep-2007 10:14 by
    In recent years the bootstrap has been successfully applied in the analysis of designed experiments, in particular in the case of replicated factorial designs. In this paper, a bootstrap-based analysis for discovering the active effects in unreplicated fractionals is proposed, taking advantage of the projection properties of the design considered. Emphasis is on the 12-run Plackett-Burman design. For each of the collapsed (replicated) complete designs, bootstrap is applied with the aim of selecting the most likely regression model. Some examples and simulations are used to illustrate ideas.
  • The estimation of the role of system & statistical thinking in decision making.

    Authors: Adler Yu., Hunuzidi E. and Shper V.
    Primary area of focus / application:
    Submitted at 3-Sep-2007 10:21 by Vladimir Shper
    The goal of this paper is to suggest very simple quantitative estimates of probabilities of wrong decisions being made by managers who don't use system and statistical thinking. The model of decision making discussed by us follows the well-known Shainin rule of green-yellow-red. It is shown that under some conditions (normal distribution of critical characteristics) the probability of wrong decisions can reach up to 50%. The analysis is based on the system archetypes suggested in our previous paper at ENBIS 2006.
  • A Control Chart for the Desirability Index

    Authors: Heike Trautmann and Claus Weihs (University of Dortmund, Dortmund, Germany)
    Primary area of focus / application:
    Submitted at 4-Sep-2007 12:21 by
    The Desirability Index is a multiobjective optimization method in
    industrial quality control, which includes a-priori preferences of the
    decision makers regarding the quality criteria and transforms the
    multiobjective into a univariate problem. Settings of the process
    influencing factors are selected that lead to the highest possible DI
    value and therefore to maximum process quality. Until now the DI was
    solely used for optimization purposes. A straightforward question
    however is if the maximum DI value can be maintained in the ongoing
    process. For this purpose a DI control chart is introduced, which proves
    to be superior compared to existing charts. Additionally an innovative
    procedure for the analysis of out-of-control signals is presented.
  • Accuracy of the End-to-End performance estimation in logistic service environments

    Authors: Klaus-Ruediger Knuth (Quotas GmbH, Hamburg, Germany)
    Primary area of focus / application:
    Submitted at 4-Sep-2007 13:12 by
    An important part of the QoS in logistic services is the ability to distribute items from one point to the other within a defined timeframe. This is called the service standard. Systems that measure the compliance to this standard are often panel based.

    The result of the measurement can take for example the following form:
    "In 2005 95% of all letters sent from sender panellists have been received by receiver panellists on the next day of service".

    All measurement systems are sized according to given accuracy requirements. Basis is an appropriate estimation of the variance of the on-time performance estimator.

    CEN, the European standardisation network, has almost finished the devellopment of recommendations on the calculation of this variance. Special difficulties that have to be overcome were:

    · All items for any sender, any receiver and any sender-receiver relation may be correlated;

    · On-time performance is usually on a level well above 90% where simple nor-mal approximation is weak;

    · The sampling design is usually disproportional leading to weighted results.

    Specifics: Oral presentation
  • Monitoring Nonlinear Profiles using Support Vector Machines

    Authors: Stelios Psarakis, Javier M. Moguerza and Alberto Munoz
    Primary area of focus / application:
    Submitted at 4-Sep-2007 15:30 by
    Support Vector Machines (SVMs) are powerful classification and regression procedures. SVMs arose in the early nineties as optimal margin classifiers in the context of Vapnik’s Statistical Learning Theory. During the last few years SVMs have been successfully applied to real-world data analysis problems, usually providing improved results compared to other techniques. This methodology can be used within the Statistical Process Control (SPC) framework. In this work we focus on the use of SVMs for monitoring techniques applied to nonlinear profiles.
  • On using bootstrap methods for understanding empirical loss data and dynamic financial analysis

    Authors: Grigore ALBEANU (UNESCO Chair in Information Technologies at University of Oradea), Henrik Madsen (IMM, DTU), Manuela Ghica (Spiru Haret University), Poul Thyregod (IMM, DTU) and F. Popentiu-Vladicescu (Univ. of Oradea)
    Primary area of focus / application:
    Submitted at 4-Sep-2007 15:34 by
    Computer-Intensive methods for estimation assessment provide valuable information concerning the adequacy of applied probabilistic models. The bootstrap method is an extensive computational approach for understanding empirical data and is based on resampling and statistical estimation. It is a powerful tool, especially when only a small data set is used to predict the behaviour of systems or processes. This paper describes some case studies based on the Efron type bootstrap approaches [1] for modelling loss distributions [2] and for general dynamic financial analysis [3]. The case studies are inspired from risk management field. The research is based on theoretical previous developments in accuracy assessment [4], reliability estimation [5] and risk exchange modelling [6].


    [1] Efron B., Computer-Intensive Methods in Statistical Regression, Siam Review, 30, 3, 421-449, (1988).

    [2] Hogg, R.V. and Klugmsn S.A,: Loss distributions, John Wiley & Sons, New York, 1984.

    [3] Kaufmann R., Gadmer A. and Kett R.: Introduction to dynamic functional analysis, ETH Zurich, IFOR, 1999, http://www.ifor.math.ethz.ch/publications/1999_dynamicfinancialanalysis.pdf .

    [4] Albeanu G.: Resampling Simultaneous Confidence Bands for Nonlinear Explicit Regression Models, Mathematical Reports, 50(5-6), 289-295, (1998).

    [5] Albeanu G. and Popentiu F.: On the Bootstrap Method: Software Reliability Assessment and Simultaneous Confidence Bands, Annals of Oradea University, Energetics Series, 7(1), 109-113, (2001).

    [6] Manuela Ghica.: A risk exchange model with a mixture exponential utility function, Annals of Spiru Haret University, Mathematics and Informatics Series, 2006.