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:

Risk Valuation for European Electricity Trading with Hourly Pricing Forward Curves
Authors: Martin Rainer (ENAMEC Institut und FZRM Univ. Würzburg, SYNECO Trading GmbH, München)
Primary area of focus / application: Modelling
Keywords: Pricing forward curves, Electricity markets, Risk valuation, Valueatrisk, Risk management
Submitted at 2Jul2014 17:47 by Martin Rainer
Accepted
In these markets the spot prices are quoted for each hour of a day. Furthermore, traded forward instruments for base and peak load provide a basis of defined forward risk factors. It is common practice to derive hourly PFC (hPFC) curves from the latter and estimated daily and hourly profiles. The details of this construction may genuinely
affect the correlation structure of the hPFC.
An additional problem is the existence negative market prices, because it contradicts the standard assumptions of conventional valueatrisk (VaR) methodology. This in particular requires a modification of the VaRconcept of risk management.
Several approaches for this purpose are discussed from both perspectives of theoretical foundations and practical viability. 
Structured Data Analysis with Regularized Generalized Canonical Correlation Analysis
Authors: Arthur Tenenhaus (Supelec)
Primary area of focus / application: Mining
Keywords: Regularized generalized canonical correlation analysis, Structured data analysis
Submitted at 1Aug2014 16:49 by Arthur Tenenhaus
Accepted

Multivariate Latent Variable Models: Misconceptions and Industrial Applications
Authors: John F. MacGregor (McMaster University and ProSensus, Inc.)
Primary area of focus / application: Modelling
Keywords: Multivariate latent variable models, Industrial application, Projection to latent structure
These properties allow for many interesting industrial applications. Several of these will be illustrated in this presentation for industrial batch processes, including: (i) the analysis and interpretation of batch production data, (ii) the optimization of batch operations, and (iii) the monitoring and control of batch processes. 
A BootstrapBased Correction Method for the Hotelling’s Multivariate Control Chart for Serially Dependent Data
Authors: András Zempléni (Eötvös Loránd University, Budapest), László Varga (Eötvös Loránd University, Budapest)
Primary area of focus / application: Process
Keywords: Block bootstrap, Effective sample size, Hotelling’s control chart, Serial dependence
Submitted at 6Aug2014 14:00 by András Zempléni
Accepted
The effective sample size n_e is defined as the size of the independent sample, for which the trace of the covariance matrix is the same as the one of the empirical data. The critical values are then the usual critical values of the Hotelling’s chart, but based on n_e observations instead of the original n.
The block bootstrap methodology (see [2]) is an effective tool for resampling serially dependent data. However, the determination of the block size is by far not obvious. We propose a method, where n_e is determined by two approaches: one is modelbased (vectorautoregressive or GARCH models are fitted to the data), the other is by the block bootstrap, where the block size is chosen, for which the simulated trace of the covariance matrix is the nearest to the one given by the model. This block size may then be used for simulations, e.g. for bootstrap confidence regions.
References
[1] P. Rakonczai, L.Varga and A.Zempléni (2014): Copula fitting to autocorrelated data, with applications to wind speed modeling. Annales Univ. Sci. Budapest, Sect. Comp., to appear.
[2] S. N. Lahiri (2003): Resampling Methods for Dependent Data, Springer. 
DoE Empowerment
Authors: Stefanie Feiler (AICOS Technologies Ltd.), Philippe Solot (AICOS Technologies Ltd.)
Primary area of focus / application: Design and analysis of experiments
Keywords: DoE , Software, STAVEX, DoE propagation, DoE education
However, many researchers still show a certain reluctance to employ DoE.
They judge the method to be complicated, suspect that they would be forced to perform too many experiments, or believe that it can only be mastered by statistics experts.
The strength of the DoE expert system STAVEX is its userfriendliness.
Users are guided in selecting an appropriate design for their specific situation, in the model analysis, and in deciding on the next experimental step. This allows the tool to be (correctly) used by "standard" researchers, without having to rely on the help of a statistician. We illustrate the various aspects with reallife examples. 
The ReNewTown Project and the Importance of (Self)Assessment
Authors: Irena Ograjenšek (Faculty of Economics, University of Ljubljana)
Primary area of focus / application: Modelling
Keywords: ReNewTown, Lessons learned, Central Europe Programme, (Self)assessment
Submitted at 15Sep2014 11:34 by
Accepted