ENBIS: European Network for Business and Industrial Statistics
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ENBIS12 in Ljubljana
9 – 13 September 2012 Abstract submission: 15 January – 10 May 2012The following abstracts have been accepted for this event:

Cyborg Spring: Social/Media/Revolution
Authors: Robert Kozinets (York University)
Primary area of focus / application: Mining
Keywords: social media, netnography
Submitted at 11Jun2012 10:22 by Robert Kozinets
Accepted

On the Many Faces of Text Processing
Authors: Marko Grobelnik (Inštitut Jožef Stefan)
Primary area of focus / application: Mining
Keywords: textual data, text processing
Submitted at 11Jun2012 10:25 by Marko Grobelnik
Accepted

Bayesian Analysis of Shortterm Directional Data for Wind Potential Assessment
Authors: Pasquale Erto (University of Naples Federico II), Antonio Lanzotti (University of Naples Federico II), Antonio Lepore (University of Naples Federico II)
Primary area of focus / application: Modelling
Keywords: Bayesian analysis, directional analysis, windfarm layout, wind speed distribution

Nonparametric Control Charts: The Data Depth Approach
Authors: Giovanni Porzio (University of Cassino), Giancarlo Ragozini (University of Naples Federico II)
Primary area of focus / application: Process
Keywords: control charts, processes, nonparametric statistics, simplicial depth, convex hull probability depth
Submitted at 12Jun2012 11:04 by Giovanni Porzio
Accepted
Aim of this work is thus to provide an overview of a special type of nonparametric control charts, the data depth control charts. Data depth is a function that measures the centrality of a point with respect to a given multivariate distribution. The deepest points lie at the core of the distribution, while points with lower depths are located in the distribution tails. In the multivariate statistical process control setting, the deepest points will correspond to items of higher quality, under the assumption that the center of the process distribution is the quality target to be achieved.
Although very attractive, the use of data depth charts has been somewhat limited by the computational efforts required to implement them in practice. At least, this seems a drawback for the charts based on the simplicial depth. Alternatively, the adoption of charts based on the convex hull probability depth is encouraged. 
A Nonparametric Multivariate Location Control Chart for Angular Symmetric Distributions
Authors: Amor Messaoud (University of Tunis), Giovanni Porzio (University of Cassino), Giancarlo Ragozini (University of Naples Federico II)
Primary area of focus / application: Process
Keywords: control charts, processes , nonparametric statistics, directional symmetric distribution
Submitted at 12Jun2012 11:13 by Amor Messaoud
Accepted
We highlight that multivariate normal is a special case of a directional symmetric distribution. Hence, it seems worth to develop nonparametric charts that merely assume the process distribution is directional symmetric. Furthermore, we note that directional symmetry is equivalent to halfspace symmetry in the case of continuous distributions (Zuo and Serfling, 2000). Consequently, a whole class of alternative median estimators may be considered. Furthermore, one may exploit alternative geometrical properties of such distributions to design a proper control chart.
In this work, we observe that in the case of directional symmetry the width of the angles that each consecutive observation yields in a sequence of incontrol data are uniformly distributed. This allows us to design a simple angle chart that seems to be very effective in detecting shift in the process location. In addition, we note that this chart is insensitive to change in the process covariance structure. Consequently, the proposed chart may be also used after a signal in a nonparametric scale chart in order to discriminate between possible outofcontrol causes.
References:
Zou, C. and Tsung, F. (2011) A Multivariate Sign EWMA Control Chart. Technometrics, 53, pp. 8497.
Zuo, Y. and Serfling, R. (2000). On the performance of some robust nonparametric location measures relative to a general notion of multivariate symmetry. Journal of Statistical Planning and Inference, 84, pp. 5579. 
Use of Targeted Bayesian Network Learning for Suspects Identification
Authors: A. Gruber (Tel Aviv University, Department of Industrial Engineering and Management), S. Yanovski (Tel Aviv University, Department of Industrial Engineering and Management), I. BenGal (Tel Aviv University, Department of Industrial Engineering and Management)
Primary area of focus / application: Mining
Keywords: Bayesian classifier, Targeted Bayesian Network, suspect activities, communication network
Submitted at 18Jun2012 09:14 by Irad BenGal
Accepted
The application was performed using a learningbased model by the targeted Bayesian network learning (TBNL) method. The underlying principle of this method is that it attempts to best approximate the marginal probability distribution of a predetermined target variable, depending on other attribute variables within the domain. This TBNL algorithm enables an efficient management of the tradeoff between the model’s complexity and the model’s classification accuracy, by using information theory metrics.
Our results show that the TBNL fulfills the requirement of 50% sensitivity rate (Recall) with at most 1% false positive rate (FPR). These results, which reflect behavioral patterns, show that the method reduces the FPR by 50% for a required sensitivity level compared with other Bayesian classifiers.