ENBIS: European Network for Business and Industrial Statistics
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ENBIS13 in Ankara
15 – 19 September 2013 Abstract submission: 5 February – 5 June 2013The following abstracts have been accepted for this event:

Teaching Exploratory Data Analysis by Using the Data from the Red Bead Game
Authors: Vladimir Shper (Moscow Institute of Steel & Alloys), Yuri Adler (Moscow Institute of Steel & Alloys), Elena Hunuzidi (Moscow Institute of Steel & Alloys), Olga Maximova (Moscow Power Institute)
Primary area of focus / application: Education & Thinking
Keywords: Teaching,, statistical, methods,, exploratory, data, analysis

The Statistics: Past, Present, and Future (A New Paradigm in Statistics)
Authors: Yury Adler (Moscow Institute of Steel & Alloys)
Primary area of focus / application: Education & Thinking
Keywords: paradigm, shift, statistics, future
2. New paradigm leads to new rules and new behavior.
3. Teamwork together with experts from different areas on the real projects.
4. Mainstream – operational definitions versus axiomatic.
5. Visualization versus formalization.
6. Design of experiments as a base of statistics.
7. Data mining as a way to computerization.
8. Statistics as the language of science. (K. Pearson, G. Taguchi, H. Tsubaki, V. Nalimov).
9. New approach to education and training.
10. Statistics as a part of common culture. (H. Wells). 
Can a Correction True Value be Better Known than the Measurand True Value?
Authors: Franco Pavese (Consultant)
Primary area of focus / application: Metrology & measurement systems analysis
Keywords: correction, true value, measurement, uncertainty, systematic error, systematic effects, GUM
Submitted at 28May2013 09:22 by Franco Pavese
Accepted
[1] F. Pavese, On the difference of meaning of ’zero correction’: zero value versus no correction, and of the associated uncertainties, in Advanced Mathematical and Computational Tools in Metrology and Testing IX (F. Pavese et al., Eds), vol. 9, World Scientific, Singapore, pp. 297–309, 2012.
[2] F. Pavese, An introduction to data modeling principles in metrology and testing, in Advances in data modeling for measurements in metrology and testing, BirkhauserSpringer, Boston, 2009, 1–30. 
Bayesian Estimation of Demand and Substitution Rates with Two Products
Authors: Ulku Gurler (Bilkent University), Nilgun Ferhatosmanoglu (THK University), Emre Berk (Bilkent University)
Primary area of focus / application: Other: ISBA
Keywords: Bayes estimation, Demand rate, Substitution, Poisson arrival
Submitted at 28May2013 16:01 by ulku gurler
Accepted
In this study we consider the joint estimation estimation of the demand arrivals, primary and substitute demand rates in a context of two products using a Bayesian estimation methodology. It is assumed that the demand for the products follows a Poisson process where the unknown arrival rate is a random variable.
Under a general prior distribution settings we obtain the joint posterior distributions for the demand and substitution rates. We also consider two commonly encountered special cases and provide some numerical results to illustrate the performance of the estimators. 
Optimal Design of a Machine Part Using Design of Computer Experiments
Authors: JanWillem Bikker (CQM)
Primary area of focus / application: Design and analysis of experiments
Keywords: Design of Computer Experiments, Transfer functions, Optimization, Design for Six Sigma, Simulation
Submitted at 29May2013 08:30 by JanWillem Bikker
Accepted

Burnin: Assessing Early Life Failure Probabilities with Implemented Countermeasures
Authors: Daniel Kurz (Department of Statistics, AlpenAdria University of Klagenfurt), Horst Lewitschnig (Infineon Technologies Austria AG, Villach), Jürgen Pilz (Department of Statistics, AlpenAdria University of Klagenfurt)
Primary area of focus / application: Reliability
Keywords: Bayes, burnin, ClopperPearson, countermeasure, decision theory, Generalized Binomial distribution, reliability
Submitted at 29May2013 08:41 by Daniel Kurz
Accepted
Semiconductor devices show an increased failure rate at the beginning of their life. Different procedures are applied for keeping this early life failure rate (ELFR) level at a minimum. However, in case of novel technologies, new production lines etc., the actual ELFR needs to be evaluated. This is done by stressing a sample of the produced devices for productionrelevant failures  referred to as burnin study. Based on the number of occurred failures, the early life failure probability p (as parameter of a Binomial distribution) is assessed applying ClopperPearson interval estimation and more general Bayesian estimation techniques, respectively.
In general, zero failures are required in order to meet the ppmrequirements and to successfully complete a burnin study. Once a fail occurs, the burnin study actually has to be restarted.
More efficiently, a countermeasure is implemented in the production process and its effectiveness is assessed by experts. If the countermeasure is 100% effective, the fail would not have been occurred if the countermeasure would have already been implemented before the burnin study. Therefore, it is not counted in the burnin statistics. If the countermeasure is less than 100% effective, there is just a certain probability that the fail would have been observed.
We propose a statistical model for assessing early life failure probabilities taking account of the effectiveness of the implemented countermeasure. The idea is to weight each fail according to its occurrence probability after extending the countermeasure to the process giving an advanced estimation concept for early life failure probabilities.
The model can be flexibly applied for a general number of failures and countermeasures, as well as for uncertain proportions of effectiveness. This requires the application of generalized forms of the common Binomial distribution. Moreover, a Bayesian version of the proposed model offers the possibility of integrating further expert knowledge on the failure probability.
For reasons of justification, the model is further discussed with regard to its decisiontheoretical background. The loss function, risk function and decision strategies are adapted to failures which are tackled by countermeasures. Optimality of the proposed model can be demonstrated exploiting Bayesian decision principles.
The benefit of the provided model is that the improvement measures in the chip production process are reflected in the early life failure probability without restarting the burnin study. Thus, the ppmtarget can be proven by burning an additional number of items for zero fails. Since burnin is expensive in terms of costs, time and resources, this leads to an essential improvement of burnin efficiency.
Acknowledgement:
The work has been performed in the project EPT300, cofunded by grants from Austria, Germany, Italy, The Netherlands and the ENIAC Joint Undertaking.