ENBIS: European Network for Business and Industrial Statistics
Forgotten your password?
Not yet a member? Please register
ENBIS12 in Ljubljana
9 – 13 September 2012 Abstract submission: 15 January – 10 May 2012The following abstracts have been accepted for this event:

Monitoring Multivariate Process Variability : A Unified View From Generalized Variance To Likelihood Ratio Control Charts
Authors: Emanuel Pimentel Barbosa (Universidade Estadual de Campinas), Mario Antonio Gneri (Universidade Estadual de Campinas), Ariane Meneguetti (Universidade Estadual de Campinas)
Primary area of focus / application: Process
Keywords: Control Charts, Generalized Variance, Likelihood Ratio Statistic, Multivariate Processes
Submitted at 12Apr2012 22:02 by Emanuel Pimentel Barbosa
Accepted
The first one is that its usual implementation as a 3sigma Shewhart type control chart based on normal approximation inflates substantialy the risk of false alarm, and a solution was presented (ENBIS 11) based on CornishFisher correction and Meijer Gfunction.
The second one, is the fact that the S doesn´t detect all sorts of changes in the process variancecovariance matrix Sigma, and a solution is proposed here based on the inclusion of an auxiliary statistic based on the trace(S), jointly with the S.
Since S and trace(S) are the two components of the likelihood ratio for testing hypothesis about Sigma, we propose to join these two statistics in just one, resulting a modified likelihood ratio control chart, which gives an unified wiew of the problem.
In order to ilustrate the proposed methods, a couple of numerical examples are provided. 
Robust Control Chart to Monitor the Information System of Semiconductor Production Plant
Authors: Michel Lutz (Ecole des Mines de SaintEtienne), Espéran Padonou (Ecole des Mines de SaintEtienne), Olivier Roustant (Ecole des Mines de SaintEtienne)
Primary area of focus / application: Process
Keywords: monitoring, robust statistics, time series, control chart, information system, semiconductor industry
The objective is to place these variables under statistical control (automated monitoring). As time series are considered, implying autocorrelation phenomena, a twosteps monitoring procedure is used (Montgomery, 2005): 1) time series modelling, using the HoltWinters exponential smoothing algorithm; 2) model residuals monitoring, through an individual Shewart control chart.
However, poor controls results have been encountered with basic approaches. That is why robust methods have been implemented: the Qn robust standarddeviation estimator (Rousseeuw & Croux, 1993) is used, as well as a robust HoltWinters smoothing algorithm (Croux & al., 2011). These methods are already up and running and allow a fully automated monitoring of several hundred metrics with fairly good results.
Further works are ongoing to improve results and applications:
1) Simulation: overall performances of the different methods are evaluated and compared through statistical simulation;
2) Multivariate testing: for now, the multivariate dimension of the controls is not considered. We observed this could lead to unsatisfying outcomes (contradictory monitoring results for correlated variables, false alarms). Consequently, several simultaneous testing strategies are experienced, to enhance the current method.
Methods and results will be illustrated and compared on the basis of simulated series and real case studies from the considered semiconductor production plant. 
Bayesian Approach to Assess the Probability of Detection of Defects in Nondestructive Evaluation
Authors: Severine Demeyer (CEA LIST), Frederic Jenson (CEA LIST), Nicolas Dominguez (CEA LIST)
Primary area of focus / application: Design and analysis of experiments
Keywords: propagation of uncertainty, design of experiments, Bayesian approach, Metamodelling, nondestructive testing, probability of detection
Submitted at 13Apr2012 08:49 by Séverine Demeyer
Accepted

Reliability Analysis with Warranty Data
Authors: Nikolaus Haselgruber (CIS consulting in industrial statistics)
Primary area of focus / application: Reliability
Keywords: Reliability, Warranty Analysis, Failure Rate, Incomplete Data
Since in such cases the repair actions are executed by a network of contracting partners of the OEM typically a central warranty database collects all the claims in a systematic way. It serves as a basis for the finanical assessment and refunding of the claim costs and provides important material to determine relevant reliabililty figures, such as failure rate, mean time between failures, lifetime distribution quantiles etc. The main challenge here is the fact that not all the data required for an unbiased estimation of reliability parameters are available in such a warranty data base.
This presentation introduces a new algorithm considering step by step the requirements for the reliability analysis based on warranty data with remedial measures where information is missing. Practical examples show successful applications in several industrial branches as well as the bias potential in case of ignoring relevant aspects such as incomplete, censored or truncated data. 
Statistical Modelling of a Thermal Spraying Process with Additive Dayeffects
Authors: André Rehage (Technical University Dortmund), Sonja Kuhnt (Technical University Dortmund)
Primary area of focus / application: Modelling
Keywords: Thermal Spraying Process, Generalized Linear Models, Linked Generalized Linear Models, Dayeffect
Submitted at 13Apr2012 11:09 by André Rehage
Accepted
Generalized linear models are used to model the relationship between process parameters and particle properties as well as between particle properties and coating properties. Uncontrollable or latent variables are assumed to primarily have an additive effect on the inflight particle properties. With only a few initializing experiments at the day of interest we estimate such an additive constant (Rehage et al., 2012). Using the resulting estimate we link the generalized linear models to get a more appropriate description of the thermal spraying process for the individual day.
References:
W. Tillmann, E. Vogli, B. Hussong, S. Kuhnt and N. Rudak: Relations between inflight particle characteristics and coating properties by HVOF spraying, DVSBerichte 264, ISBN: 9783871555909, 2010.
A. Rehage, N. Rudak, B. Hussong, S. Kuhnt and W. Tillmann: Prediction of inflight particle properties in thermal spraying with additive dayeffects, SFB 823 Discussion Paper 06/12, 2012. 
Designing Choice Experiments by Optimizing the Complexity Level to Individual Abilities
Authors: Vishva Danthurebandara (Catholic University of Leuven), Jie Yu (Catholic University of Leuven), Martina Vandebroek (Catholic University of Leuven)
Primary area of focus / application: Design and analysis of experiments
Keywords: Choice experiments, Individual Sequential Optimal designs, Complexity measures, Simulation study
We propose an individual sequential design approach for setting up choice experiments that take the complexity of the choice sets explicitly into account. In a simulation study we compare this approach with some more standard designs that are used for estimating the preference heterogeneity. We show that the sequential nature or our approach is very beneficial both for estimating the individual level and the population level parameters. We show that this is due to the fact that the procedure selects the individual choice sets such that they have a constant medium level of complexity which yields very informative choices.