ENBIS: European Network for Business and Industrial Statistics
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ENBIS8 in Athens
21 – 25 September 2008 Abstract submission: 14 March – 11 August 2008The following abstracts have been accepted for this event:

Multivariate Expected Improvement Using TwoSided Desirabilities
Authors: Simone Wenzel and Joachim Kunert
Affiliation: Technical University of Dortmund, Germany
Primary area of focus / application:
The usability of the approach is demonstrated with a multiobjective optimization problem from mechanical engineering. 
Modeling Mortality Pattern using Support Vector Machines
Authors: Anastasia Kostaki , Javier M. Moguerza , Alberto Olivares , Stelios Psarakis
Affiliation: Dept. of Statistics, Athens University of Economics and Business & Dept. of Statistics and Operational Research, Rey Juan Carlos University (Spain)
Primary area of focus / application:
Submitted at 14May2008 10:20 by Alberto Olivares
Accepted

A comparative study of tests for scale equality with application in industrial statistics
Authors: Marco Marozzi
Affiliation: Università della Calabria
Primary area of focus / application:
Comparing variances or other measures of scale is much harder than comparing means or other measures of location. There are two reasons for this (Boos and Brownie, 2004). The first reason is that normal theory test statistics for detecting location shifts are standardized to be robust to non normality via the central limit theorem, and then the corresponding test procedure have approximately the correct level. This is not true for normal theory test statistics for detecting scale shifts, which are not asymptotically distribution free, but depend on the kurtosis of the parent distributions. The second reason is that for mean comparisons the hypothesis that the populations may differ only in location is often appropriate allowing the use of permutation methods that have the exact level for any distributions, on the contrary for variance comparisons, the hypothesis that the populations may differ only in scale rarely makes sense since one usually wants to allow mean differences. Given that, it is necessary to adjust for unknown means or locations by subtracting means or other location measures, but the transformed data are not exchangeable and then permutation tests provide only approximately exact solutions (Good, 2000).
The literature on tests for the equality of variances is vast (Conover et al., 1981), a test which usually stands out in terms of power and robustness against non normality is the W50 BrownForsythe (1974) modification of the Levene (1960) test in which the sample median replaces the sample mean as an estimate of the location parameter. In this paper we focused on the twosample scale problem and in particular on Levene type tests. We consider ten Levene type tests: the W50 test, its bootstrap and permutation versions, the M50 (Pan, 1999) test, the L50 (Pan, 1999) test along with its bootstrap and permutation versions, the R (O’Brien, 1979) test its bootstrap and permutation versions. We consider also the F test, the modified FlignerKilleen (1976) FK test and the two approaches of Shoemaker (1995 and 1999). Typeone error rate and power of the tests are investigated. We discuss the application of the tests to real data sets in the context of quality control and industrial statistics.
Boos, D. D. and Brownie, C. (2004) Comparing variances and other measures of dispersion, Statistical Science, 19, 4, 571578.
Brown, M. B. and Forsythe, A. B. (1974) Robust tests for the equality of variances, Journal American Statistical Association, 69, 364–367.
Conover, W. J., Johnson, M. E. and Johnson, M. M. (1981) A comparative study of tests for homogeneity of variances, with applications to the outer continental shelf bidding data, Technometrics, 23, 351–361.
Fligner, M. A. and Killeen T. J. (1976) Distributionfree twosample tests for scale, Journal of the American Statistical Association, 71, 210213.
Good, P. (2000) Permutation tests, a practical guide to resampling methods for testing hypotheses 2nd ed. SpringerVerlag, New York.
Levene, H. (1960) Robust tests for equality of variances. In Contributions to Probability and Statistics (I. Olkin, ed.) 278–292, Stanford Univ. Press, Stanford.
O’Brien, R. G. (1979) A general ANOVA method for robust tests of additive models for variances, Journal of the American Statistical Association, 74, 877880.
Pan, G. (1999) On a Levene type test for equality of two variances, Journal of Statistical Computation and Simulation, 63, 5971.
Shoemaker, L.H. (1995) Tests for differences in dispersion based on quantiles, The American
Statistician, 49, 2, 17982.
Shoemaker, L.H. (1999) Interquantile tests for dispersion in skewed distributions, Communications in Statistics – Simulation and Computation, 28, 189205. 
Online Monitoring and Classification of Paper Formation using Image Analysis
Authors: Marco S. Reis, Armin Bauer
Affiliation: University of Coimbra, Voith Paper Automation
Primary area of focus / application:
Submitted at 14May2008 14:09 by Marco P. Seabra dos Reis
Accepted
In this work, we address an approach for evaluating and monitor paper formation using images acquired with an especially designed sensor, inline, insitu and in real time. The methodology essentially consists of applying wavelet texture analysis (WTA) to raw images (Bharati et al., 2004), in order to compute a wavelet signature vector for each image, based on which the discrimination of images regarding different formation quality levels can be performed. A principal component analysis (PCA; Jackson, 1991) of such features confirms the differences in formation quality levels defined a priori, from visual inspection, and, furthermore, suggests a new subclass for abnormal samples, related to the bulkiness of fibre flocks.
A PCAMSPC monitoring approach is also proposed, providing good preliminary results when applied to the available images, as analyzed with the ROC curve for the method and confirmed with a Monte Carlo study using subimages with 1/4 of the size of the original ones.
References
Bharati, M. H., Liu, J. J. & MacGregor, J. F. (2004). Image Texture Analysis: Methods and Comparisons. Chemometrics and Intelligent Laboratory Systems, 72, 5771.
Jackson, J. E. (1991). A User's Guide to Principal Components. New York: Wiley. 
Multivariate Class Prediction with Gene Expression Data
Authors: Marco S. Reis
Affiliation: University of Coimbra
Primary area of focus / application:
Submitted at 14May2008 14:12 by Marco P. Seabra dos Reis
Accepted
Gene expression data is acquired through DNA microarray technology, where genomic DNA sequences from genes immobilized in a solid matrix (probes) are hybridized with labelled mRNA representative of different cells states (targets). The magnitude of signal intensity at each probe location is then interpreted as a measure of the expression level of that particular gene, at the state corresponding to the label being analyzed.
Microarray data have been widely analyzed through univariate techniques. This class of techniques try to identify those genes that most differentiate between the states under analysis (usually two), through F and t statistics, or through other sort of univariate methodologies, such as the “signal to noise ratio” (Golub et al., 1999) and the SAM (“Significance Analysis of Microarrays”; Tusher et al., 2001) methods. The simplicity underlying these methodologies enables them to adequately control classification error rates, such has the False Positive Rate (FPR), FamilyWise Error Rate (FWER) and the False Discovery Rate (FDR). However, they do tend to disregard the cooperative behaviour of gene expression, i.e., their combined activity under cell certain conditions. This turns out to be a significant drawback of the univariate methodologies, as it is well known that gene activity is rarely an isolated result of the action of a single gene, but a consequence of a cascade of events where several genes clusters participate.
In this context, multivariate approaches offer more flexibility for describing gene coexpression patterns, but also present some methodological limitations. For instance, Fisher Discriminant Analysis (FDA) requires the number of variables (genes in microarray data) to be less than the number of observations, a condition not met in practice. Therefore, such multivariate techniques do require a preliminary stage of variable selection, usually based on univariate approaches, where data dimensionality is reduced until the necessary condition for applying multivariate methods are met. On the other hand, it is not expected that all genes participate in each physiological response, but only clusters of functionally related genes, and therefore the methods should be able to identify such clusters of genes
In this work, an intrinsic multivariate approach is presented where the preliminary variable reduction stage is not required, but that can still be conducted after a first run of the proposed methodology, on the basis of multivariate information generated in such first trial. The approach combines PLSDA and FDA (PLSDA standing for “Partial Least Squares for Discriminating Analysis”), and has incorporated a “nonclassification” analysis, enabling the assessment of the uncertainty for each class prediction, according to two distance measures of the expression profile under analysis to training dataset entities. We also propose a genes VIP (variable importance in projection) metric for the combined PLSDA/FDA methodology, in order to identify key genes segregating the different classes.
The approach is illustrated using a well known data set (Golub et al., 1999), where different expression phenotypes were measured in samples from patients with different types of leukaemia: acute lymphoblastic leukaemia (ALL), subdivided according to their lineage (ALLB and ALLT) and acute myeloid leukaemia (AML).
References
Golub, T.R., Slonim, D.K., Tamayo, P., Huard, C., Gaasenbeeck, M., Mesirov, J.P., Coller, H., Loh, M.L., Downing, J.R., Caligiuri, M.A., Bloomfield, C.D., Lander, E.S. (1999). Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science, 286, 531537.
Tusher, V.G., Tibshirani, R., Chu, G. (2001). Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. USA, (98), 51165151. 
Optimal ChangePoint Detection for EWMA Processes
Authors: S. Psarakis and A.N. Yannacopoulos
Affiliation: Department of Statistics  Athens University of Economics and Business
Primary area of focus / application:
Submitted at 14May2008 14:30 by Stelios Psarakis
Accepted