ENBIS: European Network for Business and Industrial Statistics
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ENBIS-18 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:
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Short-Term Forecasting of National Imbalance Volume
Authors: Shen Huang (EDF R&D)
Primary area of focus / application: Modelling
Secondary area of focus / application: Business
Keywords: Energy markets, Time series, Forecasting, Model selection
Submitted at 28-Mar-2018 16:55 by Shen HUANG
Accepted
In this talk, we are interested in the forecasting of UK-national net imbalance volume for the next few hours. Different statistical or machine learning models are tested such as linear regression, generalized additive models and random forest. The performance of tested models is evaluated on classical statistical criteria and also earnings/loss-based criteria, which is more meaningful for traders; according to our earnings/loss-based criteria, the annual financial gain made by our best forecast model is estimated to several million British Pounds. A comparison of those tested models will be provided and some further improvements of net imbalance volume forecasts will also be discussed. -
Variance-Sensitive Cost-Optimal Control Charts for Healthcare Data
Authors: András Zempléni (Eötvös Loránd University, Budapest), Balázs Dobi (Eötvös Loránd University)
Primary area of focus / application: Process
Keywords: Control chart, Cost-effectiveness, Healthcare, Markov-chain
Further developing the method, we introduce a target function to be minimised, which also incorporates the variance of the cost, which is often very important from a process control viewpoint. The resulting model requires several parameters to be estimated, and the accuracy of these estimations may have a significant effect on the results. Because of this, we investigate the sensitivity of the optimal parameters (the critical value and the sampling interval), and the resulting average cost and cost variance on different parameter values. We demonstrate the usefulness of the approach for real-life data of patients treated in Hungary – e.g. monitoring the cholesterol level of patients with cardiovascular event risk.
Reference
[1] B. Dobi, A. Zempléni, Cost-optimal Control Charts for Healthcare Data. 17th Annual Conference of the European Network for Business and Industrial Statistics, Naples, 2017. -
Global Sensitivity Analysis and Bayesian Calibration of a Clogging Numerical Model
Authors: Bertrand Iooss (EDF R&D), Loïc Le Gratiet (EDF R&D), Guillaume Damblin (CEA), Sandrine Gyuran (CEA), Laurent Lefebvre (Framatome), Mathieu Segond (Framatome), Roberto Spaggiari (Framatome)
Primary area of focus / application: Design and analysis of experiments
Secondary area of focus / application: Modelling
Keywords: Calibration, Clogging, Metamodel, Sensitivity analysis, Steam generators, Uncertainty
Submitted at 29-Mar-2018 08:57 by Bertrand Iooss
Accepted
The objective of this work is to improve the modeling of clogging phenomenon to increase the predictive capability of the computer code. A global sensitivity analysis, based on Sobol’ indices, is first performed by the use of a neural network metamodel that has learnt on several runs of the computer code. By discussing the results with the clogging specialist engineers, this step helps improving the understanding of the clogging phenomenon. A Bayesian calibration of an epistemic model parameter is then applied in order to fit simulations to data. The resulting model allows compensating for physical phenomena not taken into account by the initial clogging numerical model. -
Performance of Bayesian Weibull Credible Intervals for Weibull Modulus in Small Samples
Authors: Meryem Yalçinkaya (Kirikkale University), Burak Birgören (Kirikkale University)
Primary area of focus / application: Reliability
Keywords: Weibull modulus, Bayesian estimation, Classical estimation, Credible interval, Confidence interval, Prior elicitation, Monte Carlo simulation
Submitted at 29-Mar-2018 09:23 by MERYEM YALÇINKAYA
Accepted
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qgam: Quantile Additive Regression Models in R
Authors: Matteo Fasiolo (University of Bristol), Yannig Goude (EDF R&D), Raphaël Nedellec (EDF R&D), Simon N. Wood (University of Bristol)
Primary area of focus / application: Other: Electricity Data Analysis with R
Secondary area of focus / application: Modelling
Keywords: Quantile regression, Electricity load forecasting, Statistical software, Generalized additive models, R package
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Implementation of Standardised Multivariate Capability Indices in R
Authors: Emilio L. Cano (University of Castilla-La Mancha), Matías Gámez Martínez (University of Castilla-La Mancha), Noelia García Rubio (University of Castilla-La Mancha)
Primary area of focus / application: Process
Keywords: Capability analysis, Multivariate process capability indices, Multivariate analysis, Statistical software, ISO standards, Statistical Process Control
In this work, the process capability indices for characteristics following a multivariate normal distribution defined in the ISO 22514-6 International Standard are implemented in the R Statistical Software and programming language. The methods are illustrated with the numerical examples included within the own Standard.Therefore, the Open Source developed software can be easily checked against the Standard, both via the source code and the results on example data. At the end, compliance can be assured to third parties, overcoming the main barrier for free-software-skeptical SPC practitioners.