ENBIS: European Network for Business and Industrial Statistics
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ENBIS11 in Coimbra
4 – 8 September 2011 Abstract submission: 1 January – 25 June 2011The following abstracts have been accepted for this event:

Optical coherence tomography data analysis by support vector machines
Authors: P. Serranho, P. Rodrigues, R. Bernardes
Affiliation: IBILI, Faculty of Medicine, University of Coimbra and AIBILI  Association for Innovation and Biomedical Research on Light and Image
Primary area of focus / application: Mining
Keywords: Support Vector Machines , Segmentation , Optical Coherence Tomography , Medical Imaging , Classification , Automatic Training
We propose the use of support vector machines (SVM) as the basis for the segmentation of OCT retinal data. Using an appropriate set of features and an automatic labeling method for the training set based on gradient methods, we suggest a fully automatic procedure to classify each voxel of the OCT retinal volume as vitreous humour, upper retina, retinal pigment epithelium or choroid.
Moreover, we will also illustrate the use of SVM to classify OCT volume data into healthy, diabetic retinopathy or diabetic macular edema eye. The results present a nice rate of correct classification by a leaveoneout procedure. 
Application of a Six Variable Mixture Test Design (With a NonMixture variable)
Authors: William Bettis Line
Affiliation: DOES Institute
Primary area of focus / application: Design and analysis of experiments
Keywords: DOE , Mixture Experiments , Consumer responses , RoundRobin Test Method , Product Optimization , Statistical Models
Submitted at 28Apr2011 23:18 by William Line
Accepted
The test design consisted of 31 specially selected recipes specially selected to be representative of all possible recipes. Test products were made using each recipe. Each recipe was compared directly to a ‘standard’ recipe  the current recipe at that time. Over 70 quality measures, R&D product variables, and consumer response variables were analyzed on each product. Statistical data models were used to find the optimum product recipe.
A taste test using a national probability sample of candy consumers was conducted to find their ‘best recipe’. Using a roundrobin test design, each consumer tasted two products  a test recipe product and the standard product and indicated their preference. Statistical models were fitted to the data to find the consumer rating variability and to find the optimum consumer recipe that also met quality and R&D standards.
The project results included discovering an optimum recipe that was superior to the current recipe used at that time. The conclusions were applied to ingredient recipes in three different worldwide bestselling candy products. The optimized recipe has been used successfully for many years.
This project showed that a statistically designed mixture experiment with a nonmixture component can be used to find an optimum product according to consumers.
The author acknowledges the contributions of Dr. George E. P. Box for his suggested statistical test design and his statistical analysis, particularly in the multiresponse optimization phase to find the best recipe.
Bibliography
Box, G.; Draper, N.; Empirical Model Building and Response Surfaces, Wiley, 1987. 
Application of a Second and Third Order Test Design in Six Dimensions with an Orthogonality Constraint
Authors: Norman R. Draper, PhD Michael J. Morton, PhD William Bettis Line
Affiliation: DOES Institute
Primary area of focus / application: Design and analysis of experiments
Keywords: DOE , Higher Order Models , Orthogonality Constraint , NASA Application , Aerodynamic Parameters , Wind Tunnel Tests , Force Moment Balance
The NASA application described in this paper was in the wind tunnel testing of aerodynamic forces. In the wind tunnel testing of aircraft, including the space shuttle, a force moment balance is used to test aerodynamic effects. The Wright brothers developed the first force moment balance in 1903. Many force balances are used today, including several by NASA, Lockheed Martin, and many universities.
Previous to this test design, the onefactoratatime method of testing was used at NASA to calibrate balances. This paper presents a statistical test design that was developed in 2001 for NASA. The project showed that using a designed test reduced the cost of calibration by 85%. The resulting method also improved data quality. NASA patented the developed system, despite their longstanding policy of making innovations ‘public domain’.
The overall design displayed in design units will be presented. The orthogonality constraint will be shown, plus the ANOVA table. It is often desired to run a calibration sequentially That is, a 2nd order design can be run first. If a lack of fit occurs, that design is followed by a set of axial points to give the 3rd order segment. The recommended test design, including 2nd order and 3rd order segments, will be presented.
In aerospace and defense testing, the use of 2nd order models has been ineffective. There have been reported ‘many discontinuities in the factor space.’ There exists a need for higher order models in the future, as suggested by this application.
Draper, N. R.; Smith S. Applied Regression Analysis, 3rd Edition. Wiley, April 1998
Box, G.; Draper, N.; Empirical Model Building and Response Surfaces. Wiley, 1987.
Box, G. E. P., Hunter, W .G., Hunter, J. S.; Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building. Wiley, 1978. 
Bayesian Variance Separation Under Heteroscedasticity – Pressure Measurement as Case Study
Authors: Katy Klauenberg, Karl Jousten and Clemens Elster
Affiliation: PhysikalischTechnische Bundesanstalt (PTB), Abbestr. 212, 10587 Berlin, Germany
Primary area of focus / application: Modelling
Keywords: linear mixed model , multivariate normal distribution , Bayesian Approach , Jeffreys prior
Submitted at 29Apr2011 10:40 by Katy Klauenberg
Accepted
A standard setup of this measurement problem corresponds to a (balanced) linear mixed model. We want to estimate the variability of the device measurements as well as the variability of the measurand (captured by random errors and random effects respectively in the linear mixed model).
This is a standard statistical model which has been extensively treated for known variances as well as for unknown but equal device variances. For an overview on classical as well as Bayesian solutions, see for example [2]. However, different devices (or labs) rarely exhibit the same variability. We are therefore bound to consider a full heteroscedastic model.
Reformulated, the measurements can simply be viewed as ni identical replications originating from an njvariate normal distribution with full covariance matrix of known structure. Fitting a multivariate normal distribution to data has been discussed frequently. For an (objective) Bayesian point of view generally accounting for the full covariance matrix, see for example [3]. However, for the specific (parametric) covariance structure above, no Bayesian approach appears to be available.
We employ a Bayesian approach using the noninformative Jeffreys prior to infer the full distribution of the variance parameters in a heteroscedastic linear mixed model. By way of simulation, we show that the Bayesian estimates reproduce underlying parameters well, better than Maximum Likelihood Estimates do. Our results are insensitive to small changes in prior assumptions. Moderate violations of the normality assumption for the measurand have no effect on the estimation of the variability of the appliance measurements. We will additionally demonstrate the effect of investments into resources, i.e. how the accuracy improves with an increasing number of participating devices or labs nj.
We will present results of fitting the above model to measurements of ni = 16 pressures by nj = 4 appliances each, performed at the PTB. The estimated variability for some devices is substantially smaller than the observed variance of their measurements. This case study demonstrates the full potential of variance separation under heteroscedasticity.
References
[1] K. Jousten and S. Naef, Journal of Vacuum Science & Technology A 29(1), 0110111 (2011), DOI:10.1116/1.3529023.
[2] W. J. Browne and D. Draper, Bayesian Analysis 1, 473 (2006), DOI:10.1214/06BA117.
[3] D. Sun and J. O. Berger, Objective Bayesian Analysis for the Multivariate Normal Model, in Bayesian Statistics 8, eds. J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, A. F. Smith and M. West (Oxford University Press, 2007). 
Comparison of Online Design of Experiments Methods on Physical Models
Authors: Koen Rutten Josse De Baerdemaeker Bart De Ketelaere
Affiliation: Laboratory of Mechatronics, Biostatistcs and Sensors, Department Biosystems, KULeuven, Kasteelpark Arenberg 30, 3001 Heverlee
Primary area of focus / application: Design and analysis of experiments
Keywords: DOE , optimization , EVOP , RSM , Simplex , sequential optimization
Submitted at 29Apr2011 12:08 by Koen Rutten
Accepted

A Robust Approach for Calibration of NearInfrared Spectra
Authors: Walid Gani and Mohamed Limam
Affiliation: LARODEC, ISGT, University of Tunis
Primary area of focus / application: Modelling
Keywords: Calibration , data preprocessing , DOSC , NIR spectroscopy , LVR , SVR
Submitted at 29Apr2011 12:59 by Walid Gani
Accepted