ENBIS: European Network for Business and Industrial Statistics
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ENBIS18 in Nancy
2 – 25 September 2018; Ecoles des Mines, Nancy (France) Abstract submission: 20 December 2017 – 4 June 2018The following abstracts have been accepted for this event:

A Review of Multiband Image Fusion Methods With a Specific Attention to Bayesian Methods
Authors: JeanYves Tourneret (University of Toulouse)
Primary area of focus / application: Other: Keynote Lecture
Keywords: Image fusion, Pansharpening, Bayesian inference, Sparse estimation, Sylvester matrix equation
Submitted at 4Jun2018 17:49 by JeanYves Tourneret
Accepted

Disaggregated Electricity Forecasting Using Clustering of Individual Consumers
Authors: Jairo Cugliari (Université de Lyon), Benajmin Auder (Université Paris Sud), Yannig Goude (Université Paris Sud / EDF R&D), JeanMichel Poggi (Université Paris Sud, Université Paris Descartes)
Primary area of focus / application: Other: R
Secondary area of focus / application: Other: R
Keywords: Time series, Wavelets, Electricity demand, Clustering, R
The shape of the curves exhibits rich information about the calendar day type, the meteorological conditions or the existence of special electricity tariffs. Using the information contained in the shape of the load curves, [1] proposed a flexible nonparametric functionvalued forecast model called KWF (Kernel+Wavelet+Functional) well suited to handle nonstationary series.
In [2] we applied this strategy to a dataset of individual consumers from the French electricity provider EDF. A substantial gain
of $16$ \% in forecast accuracy comparing to the 1cluster approach is provided by disaggregation while preserving meaningful classes of consumers.
This project's aim is to evaluate the upscaling capacity of the strategy developed in [2] to cope with the upgrowing volume of data. For this, we explore different strategies with simulated datasets ranging from thousands to tens of millions of consumers. Our experiments show that no sophisticated computing technology is needed to solve this problem.
A R package is under development (available in Github: github.com/cugliari/iecclust) where our strategies are implemented.
[1] A. Antoniadis, X. Brossat, J. Cugliari, and J.M. Poggi. P\évision d'un processus à valeurs fonctionnelles en présence de non stationnarités. Application à la consommation d'électricit\é. Journal de la Société Française de Statistique, 153(2):52  78, 2012.
[2] J. Cugliari, Y. Goude, and J.M. Poggi. Disaggregated electricity forecasting using waveletbased clustering of individual consumers. In Energy Conference, IEEE International, 2016. 
Reliability Engineering  Challenges and Opportunities
Authors: Anan Halabi (KLATencor)
Primary area of focus / application: Reliability
Keywords: Reliability engineering, Development cycles, Reliability problems, Reliability methods
Submitted at 4Jun2018 19:01 by Anan Halabi
Accepted

Generative adversarial nets and Cerema AWP dataset
Authors: Seck Ismaila (Insa Rouen Normandie), Loosli Gaëlle (Université ClermontAuvergne)
Primary area of focus / application: Other: invited session
Keywords: Deep learning, Computer vision, Generative models, Generative adversarial nets, Cerema AWP dataset

Handling Error in Variables in Linear and Quadratic Regression Using a Stochastic Gradient Method: Application to State Estimation in Power Grids
Authors: Stephane Chretien (National Physical Laboratory), Paul Clarkson (National Physical Laboratory)
Primary area of focus / application: Modelling
Secondary area of focus / application: Metrology & measurement systems analysis
Keywords: Error in variables, Stochastic gradient, Composite estimation, Power grids, Semidefinite programming relaxation
Submitted at 4Jun2018 20:01 by Stephane Chretien
Accepted
y_i = b_0^tX_ib_0 + epsilon_i
where X_i, i=1,...,n are matrices of order p. This type of quadratic measurements are of paramount relevance in many industrial problems, such as e.g. power grid monitoring. Such problems can sometimes be efficiently studied via a convex relaxation based on SemiDefinite programming (SDP), which can be formulated as the following optimisation problem
min sum_{i=1}^n \ (y_i\text{trace}(X_iB))^2
under the constraint that B is a positive semidefinite matrix of order p. One of the standard ways to look at this problem is to perform the estimation conditionally on the covariates and derive finite sample or asymptotic properties of the estimator.
In many statistical studies, however, practitioners have to take into account the variability of the covariates and provide a consistent estimator of b0 without prior information about the variance of these covariates (Zellner 1970). The corresponding setting is often known as regression with "errors in variables". Various approaches have been proposed for this problem based on the ideal of total least squares minimisation; see van Huffel (2013) for an exhaustive overview of the problem. The Bayesian approach has also been studied by Florens (1974), for instance.
The goal of our work is to address the problem of regression with error in variables using an efficient and scalable stochastic gradient method. In the case of quadratic measurements, we will consider a SemiDefinite Programming relaxation of the quadratic leastsquares problem. These problems are reformulated as estimation in a model where expectations are composed with nonlinear functionals. We will follow a methodology developed by Wang (2014) based on a new stochastic gradient approach. One of the main advantages of the methods developed by Wang (2014) is their inherent scalability for very large problems, a feature which is not shared by most standard generalised eigenvalue based methods.
Our main contribution is an improved algorithm which extends the method of Wang (2014). Our method is able to handle both the linear setting and the SemiDefinite relaxation of the quadratic setting. Extensive simulation experiments illustrate the efficiency of our algorithm. 
Are the Ashtabula Fish Still Sick? – A Bayesian Bioequivalence Answer
Authors: Tim Robinson (University of Wyoming)
Primary area of focus / application: Other: US Invited Session
Secondary area of focus / application: Consulting
Keywords: Bayes, Bioequivalence testing, Logistic regression, Environmental, Contamination, Remediation
Submitted at 4Jun2018 20:03 by Tim Robinson
Accepted