ENBIS: European Network for Business and Industrial Statistics
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ENBIS15 in Prague
6 – 10 September 2015; Prague, Czech Republic Abstract submission: 1 February – 3 July 2015The following abstracts have been accepted for this event:

Newsvendor Model in Presence of Correlated Discrete Demand
Authors: Christian Weiß (Department of Mathematics and Statistics, Helmut Schmidt University Hamburg), Layth C. Alwan (Sheldon B. Lubar School of Business, University of WisconsinMilwaukee)
Primary area of focus / application: Business
Secondary area of focus / application: Modelling
Keywords: Newsvendor model, Discrete demand, INAR(1) model, Costoptimal orders, Approximations
Submitted at 19Mar2015 08:08 by Christian Weiß
Accepted
In this presentation, we propose the implementation of the predictive INAR(1) methodology for establishing the newsvendor order quantity for each forthcoming period. After having briefly introduced the general INAR(1) model, we provide a real case application about blood demand collected from a large regional hospital in southeastern Wisconsin. We consider the traditional newsvendor model and contrast it with the implementation based on the INAR(1) model. We conduct a comparative analysis to investigate the benefits of the newly proposed approach. 
How to Understand Complex Datasets Using Graphical Approaches
Authors: Bertram Schäfer (Statcon), Sebastian Hoffmeister (Statcon)
Primary area of focus / application: Education & Thinking
Secondary area of focus / application: Modelling
Keywords: Graphical modeling, Data exploration, Software requirements, Interactive graphs
Submitted at 27Mar2015 13:52 by Bertram Schäfer
Accepted
The learning based on simple descriptive uni or bivariate graphs is often limited to exploration and not used for a tentative data understanding and thus a first step into modeling. Graphical methods are only rarely used as a tool to help in the process of generating hypothesis. The Interaction between different graphs as well as between graphs and the data table are a necessity in this respect. 
A Comparison of the Predictive Power of Response Surface Designs and Neural Networks
Authors: Matthew Dodson (University of Michigan), Rene Klerx (SKF Group Six Sigma), Mark Tooley (Siena Heights University)
Primary area of focus / application: Modelling
Secondary area of focus / application: Six Sigma
Keywords: DOE, Response surface models, Neural networks, Blended approach

A Simple Unimodal Approximation of a Sum of Independent NonIdentical Lognormal Random Variables for Financial and Other Applications
Authors: Avi Messica (The College of Management (COMAS))
Primary area of focus / application: Finance
Secondary area of focus / application: Modelling
Keywords: Sum, Lognormal, Random variables, Unimodality, Finance

House of Security (HOS) for Preventing Human Errors
Authors: Shuki Dror (ORT Braude College), Emil Bashkansky (ORT Braude College)
Primary area of focus / application: Modelling
Secondary area of focus / application: Quality
Keywords: Human Errors, QFD, TRIZ, FMECA
Submitted at 30Mar2015 08:53 by Shuki Dror
Accepted

Mixture of Experts for Sequential PM10 Forecasting in Normandy (France)
Authors: JeanMichel Poggi (University of Paris Sud  Orsay), Benjamin Auder (University of Paris Sud  Orsay), Bruno Portier (Normandie Université, INSA Rouen)
Primary area of focus / application: Modelling
Secondary area of focus / application: Mining
Keywords: Air quality, Forecasting, Mixture of experts, PM10, Sequential prediction, Environment
More generally, Air Normand has various operational tools for the analysis of episodes and for the interpretation of measures, in view of decisions. However these complementary tools, statistical or deterministic models, local or global, often supplying different forecasts especially because of the various space and time resolutions considered. In this paper, we evaluate the interest of using sequential aggregation or mixing of experts to develop decisionmaking tools for the forecasters of Air Normand.
In the context of sequential prediction, experts make predictions at each time instance, and the forecaster must determine step by step, the future values of an observed time series. To build his prediction, it/he has to combine before each instant the forecasts of a finite set of experts. To do so, adopting the deterministic and robust view of the literature of the prediction of individual sequences, three basic references can be highlighted: Clemen (1989), CesaBianchi and Lugosi (2006) and Stoltz (2010).
In the application framework at hand, empirical studies are particularly valuable and we can mention some studies. In the area of climate Monteleoni et al. (2011), in the field of the air quality Mallet (2010), Mallet et al. (2009), the use of the quantile prediction of the number of daily calls in a call center Biau et al. (2011) and finally the prediction of electricity consumption Devaine et al. (2013). These studies focus on the rules of aggregation of a set of experts and examine how to weight and combine these experts.
The contribution of our study is multiple. First of all the scope  the adaptation to the effective context of pollution engineer forecaster  but the main novelty is that the set of experts contains both:
 Experts coming from statistical models constructed using different methods and different set of predictors;
 Experts defined by deterministic models of physicochemical prediction modeling pollution, weather and atmosphere. The models are of similar nature but of different spatial and time resolutions with or without statistical adaptation;
 And finally references such as persistence, as usual.
The aforementioned studies combine "homogeneous" methods: only statistical methods or only deterministic ones. Sequential prediction allows mixing several models built on very different assumptions in a unified approach that does not require any prior knowledge about the internal way to use for each expert to generate predictions. It is therefore particularly suitable for our application.