ENBIS: European Network for Business and Industrial Statistics
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ENBIS14 in Linz
21 – 25 September 2014; Johannes Kepler University, Linz, Austria Abstract submission: 23 January – 22 June 2014The following abstracts have been accepted for this event:

On the UnionIntersection Solution to the Testing for Equivalence and NonInferiority
Authors: Fortunato Pesarin (Dept. of Statistics, University of Padua, Italy), Luigi Salmaso (Dept. of Management and Engineering, University of Padua, Italy), Eleonora Carrozzo (Dept. of Management and Engineering, University of Padua, Italy)
Primary area of focus / application: Quality
Keywords: Dependent data, Nonparametric combination, Permutation tests, Union intersection tests
The main goals of present work are: (i) to go beyond the limitations of likelihood based methods, by working in a nonparametric setting within the permutation frame; (ii) to provide an insight into those problems by observing that they can rationally be approached by two quite different principles: one based on the IUP, the other based on Roy's unionintersection principle (UIP); (iii) to provide a general multidimensional permutation solution.
IUP and UIP principles essentially differ on the role assigned to the alternative on which the inferential focus is addressed to; thus they appear as not completely and significantly comparable. In fact the UIP, which fits closer to the traditional way of testing, considers as the null hypothesis that the new effect lies within the equivalence interval. To the best of our knowledge we think that the UIP way has not been discussed in the literature mostly because no explicit likelihood based solutions have been provided for it, except perhaps for very specific cases.
By extending a result on twosided testing within the permutation frame, where two arms of the alternative are separately, albeit simultaneously, tested and two partial test statistics suitably combined, we propose such a solution for the UIP approach. To extend the solution to the multidimansional case we use the notions developed in Pesarin and Salmaso (Permutation Tests for Complex Data; Wiley, 2010) of multiaspect testing and that of nonparametric combination of several dependent permutation tests, where a given inferential problem is analyzed by at most a countable set of concurrent different partial tests, each specialized to put into evidence one aspect of interest for the analysis and their partial results combined nonparametrically with respect to their usually unknown dependence structure. To obtain practical solutions, an algorithm for the UIP way is presented. A simulation study for evaluating its main properties is also presented. 
Representation of a 1d Signal by a 0_1 Sequence and SimilarityBased Interpretation
Authors: Petra Perner (Institute of Computer Vision and applied Computer Sciences)
Primary area of focus / application: Mining
Secondary area of focus / application: Metrology & measurement systems analysis
Keywords: 1d signal analysis, Signal coding, Similarity measure, Signal interpretation
Once the signal is described by the 0_1 sequence this sequences can be taken for interpretion of the signal based on proper syntactical similarity measures. We describe the similarity measures and give results on the interpretation accuracy based on a 1d application. 
Process Optimization of a SuperFinishing Machine through Experimental Design and Mixed Response Surface Models
Authors: Rossella Berni (Department of Statistics, Informatics, Applications "G.Parenti"University of Florence), Matteo Burbui (General Electric Oil & Gas Company)
Primary area of focus / application: Design and analysis of experiments
Secondary area of focus / application: Quality
Keywords: Mixed Response Surface Model, Robust design optimization, Random effects, Manufacturing technology
By considering the modeling features, the initial dual response surface approach has been extended in order to improve: i) the compliance between experimental design and the corresponding statistical model, such as the combined array structure, Myers et alt. (1992); ii) the inclusion in the statistical model of random effects in order to evaluate the relevance of subexperimental factors or first order interactions between control and noise variables, (Khuri, 2006; Khuri and Mukhopadhyay, 2010).
Furthermore, when considering the variability sources, the connection of RSM with the inclusion of random effects is a remarkable improvement in the study of these components, e.g. variance components, which can influence the main operative variables of the production process. In particular, the search for an optimal solution with the inclusion of random effects within the fitted mixed response surface allows us to evaluate the error components linked to noise and/or subexperimental factors.
This presentation deals with process optimization for a centrifugal compressor (Berni and Burbui, 2013). More precisely, the technological problem concerns the reduction of the surface roughness of centrifugal compressor impellers through a new technology implemented by GE Oil & Gas, called superfinishing. The new technology is studied through statistical methods in order to achieve a minimization of the final roughness according to the best set of levels for the abrasive component mixture and the time process. To this end, an experimental design is planned for three different materials, e.g. three types of steel, and mixed response surface models are applied. The application of mixed models allows us to estimate random effects, useful for better controlling the process variance in a robust design approach. Within this framework, a random effect is the initial roughness, measured for each impeller vane before starting the superfinishing process. Furthermore, random effects are also included in the final optimization step.
REFERENCES
Berni, R., Burbui, M. (2013). Process optimization of a superfinishing machine through experimental design and mixed response surface models, forthcoming on Quality Engineering.
Del Castillo, E. (2007). Process optimization. New York, NY: SpringerVerlag.
Khuri, A.I. (2006). Mixed response surface models with heterogeneous withinblock error variances. Technometrics, 48: 206218.
Khuri, A.I., Mukhopadhyay, S. (2010). Response surface methodology. WIREs Computational Statistics, 2: 128149.
Myers, R.H., Montgomery, D.C., Vining, G.G., Borror, C.M., Kowalski, S.M. (2004). Response Surface Methodology: a retrospective and literature survey. Journal of Quality Technology, 36: 5377.
Myers, R.H., Khuri, A.I., Vining, G. (1992). Response surface alternatives to the Taguchi robust parameter design approach. The American Statistician, 46: 131139.
Vining, G.G., Myers, R.H. (1990). Combining Taguchi and response surface philosophies: a dual response approach. Journal of Quality Technology, 22: 3845. 
Tolerance Design with Excel
Authors: Jonathan SmythRenshaw (Jonathan SmythRenshaw & Associates Ltd)
Primary area of focus / application: Modelling
Secondary area of focus / application: Modelling
Keywords: Tolerance stack up, Worst case, Statistical tolerance (RSS), Modelling, Excel
The introduction will detail the two approaches  worst case and using a statistical approach (RSS). I will then focus on the use of the statistical approach and using Excel to create a simple tolerance model.
I would also like to examine the use of both asymmetric and symmetric tolerance using both the normal distribution and a triangle distribution.
In summary, Excel is a powerful simulation package which if programmed correctly can create powerful models for tolerance stack up. 
A New Control Charting Procedure for Monitoring Poisson Observations
Authors: Athanasios Rakitzis (University of Nantes), Philippe Castagliola (University of Nantes), Petros Maravelakis (University of Piraeus)
Primary area of focus / application: Process
Secondary area of focus / application: Modelling
Keywords: Average run length, Control charts, Markov chain, Poisson distribution, Statistical process control

Communication and Adoption Dynamics in New Product Life Cycle: The Case of Apple iPod
Authors: Mariangela Guidolin (University of Padua)
Primary area of focus / application: Business
Secondary area of focus / application: Modelling
Keywords: New product life cycle, Business forecasting, Timedependent market potential, Bass model, GuseoGuidolin model, Nonlinear Least Squares, Early adopters
Submitted at 25Apr2014 18:23 by Mariangela Guidolin
Accepted
From a statistical point of view, the estimation of the GGM only requires sales data and is performed with standard Nonlinear Least Squares techniques through the LevembergMarquardt algorithm.