ENBIS: European Network for Business and Industrial Statistics
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ENBIS Spring Meeting 2019 in Barcelona
13 – 14 June 2019 Abstract submission: 15 January – 20 May 2019The following abstracts have been accepted for this event:
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Statistical Engineering - My View
Authors: Jonathan Smyth-Renshaw (Jonathan Smyth-Renshaw & Associates Ltd)
Primary area of focus / application: Consulting
Keywords: Statistical Engineering, Sexy, Un-sexy, Not Data Science
Submitted at 27-Feb-2019 20:02 by Jonathan Smyth-Renshaw
Accepted
Applications include give-away in the food industry, automotive production, climbing equipment and house insultation.
Statistical Engineering is not Six Sigma or Lean or Big Data or Data Science. Statistical Engineering is the silver bullet to many problems as it considers the fundamental law of engineering and couples this with the discipline of statistics. Statistical Engineering is very powerful but it lacks the buzz work of Six Sigma, Lean or Big Data. The challenge is how can the 'unsexy' become 'sexy to business leaders'? -
What’s the Role of Software in Statistical Engineering?
Authors: Volker Kraft (JMP), Ian Cox (JMP)
Primary area of focus / application: Other:
Keywords: Multidisciplinary, Learning support, Knowledge encapsulation, Knowledge sharing, Empirical model building, Software
Submitted at 15-Apr-2019 11:00 by Ian Cox
Accepted
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European funds local expenditures simulation: modelling validation and global sensitivity analysis
Authors: Samuele Lo Piano (Universitat Oberta de Catalunya), Arnald Puy (Université Libre de Bruxelles), Emanuele Borgonovo (Università commerciale Bocconi), Andrea Saltelli (Universitetet i Bergen)
Primary area of focus / application: Modelling
Secondary area of focus / application: Economics
Keywords: global sensitivity analysis, modelling, model validation, machine learning, econometrics, European funds
Submitted at 29-Apr-2019 17:28 by Samuele Lo Piano
Accepted
In this contribution, we present a model we developed to simulate the local expenditures on the basis of the observed reimbursement pattern from the European Commission to the European regions. We introduced a new measurement based on the distance of yearly cumulative figures – an index of regional specificity. This parameter along others sampled in plausible uncertainty ranges are used in Monte Carlo simulations to produce expenditure distributions.
The availability of the actual expenditures invoiced to a selection of member state authorities allowed to validate our model through multi-scale benchmarking at different levels of granularity. This comparison resulted in improving the model by tuning the modelling parameters as to reduce the mismatch between the simulated and the actual incurred expenditures. Finally, global sensitivity analysis allowed to apportion the uncertainty in the modelled expenditure against the uncertainty in the input parameters.
This study shows the potential of the combined use of machine-learning type of inference and global sensitivity analysis. The attained advantage is two-fold as it allows to: i) produce a robust modelling activity; ii) flag up the areas mostly affecting the output stochastic uncertainty. -
Remaining Useful Lifetime Prediction: A case study in combining statistics and machine learning
Authors: Alessandro Di Bucchianico (Eindhoven University of Technology), Yingjun Deng (Eindhoven University of Technology), Mykola Pechenizkiy (Eindhoven University of Technology)
Primary area of focus / application: Modelling
Secondary area of focus / application: Process
Keywords: machine learning, stochastic degradation models, remaining useful life, maintenance
The proposed approach to RUL prediction will be illustrated with an industrial use case within the EU Horizon 2020 project Prophesy. -
Statistical engineering challenges from big data in industry
Authors: Biagio Palumbo (Univ. of Naples Federico II), Antonio Lepore (Univ. of Naples Federico II)
Primary area of focus / application: Process
Secondary area of focus / application: Modelling
Keywords: shipping industry, manufacturing industry, high-speed rail industry, statistical process control, functional data analysis
Submitted at 6-May-2019 19:28 by Biagio Palumbo
Accepted
The talk will focus on presenting scenarios where the authors have witnessed the virtuous integration of technological skills, traditional statistical methods and modern analytics to face challenges from complex industrial contexts.
In particular, the first scenario regards the CO2 emission prediction and monitoring problem in the shipping industry that was triggered by the recent EU regulation 2015/757 which came into force in January 2018. Shipping operators have installed on-board large-scale multi-sensor systems for daily monitoring, reporting and verification (MRV) of CO2 emissions for their individual ship. The resulting huge amount of navigation data available has opened up new challenges to translate the statistical engineering paradigm into this context.
The second scenario regards the manufacturing industry, in the view of the current revolution and modern sensing technologies that naturally benefit from the cross-fertilization of traditional statistical quality control and experiment analysis procedures with modern analytics and data visualization tools. In particular, we aim to provide value to the context of modern additive manufacturing processes, according to the statistical engineering paradigm.
The third scenario concerns the high-speed rail industry that is acting as a catalyst for industry growth. To improve the performance of critical systems, railway companies have been collecting and storing data to monitor rolling stock, understand his behaviour, and improve his reliability, maintenance and safety. In particular, we introduce a statistical approach for detecting anomalies in critical systems and to allow operators to take prompt actions to reduce, or even avoid, the occurrence of failures. -
Individual-level patterns of perceived stress throughout the migraine cycle: A longitudinal cohort study using daily prospective data
Authors: Marina Vives-Mestres (Universitat de Girona), Serena L. Orr (Pediatrics, University of Calgary), Stephen Donoghue (Curelator, Inc.), Kenneth Shulman (Curelator, Inc.), Alec Mian (Curelator, Inc.)
Primary area of focus / application: Modelling
Keywords: migraine, personalized medicine, cluster, stress, headache, likert response variable
Submitted at 17-May-2019 17:58 by Marina Vives-Mestres
Accepted
Methods: Individuals with migraine registered to use the mobile headache diary N1-Headache® and completed 90 days of daily data
entry, including perceived stress, rated on a 0-10 scale. Days were categorized into phases: P1 = prodromal (2 days prior to the first
day with migraine), P2 = migraine (migraine days per International Classification of Headache Disorders-3 definition), P3 =
postdromal (2 days following the last migraine day with migraine), P0 = interictal days (other days). Individuals with at least 5 days
in each phase were eligible and data from their first 90 days were used. The odds of stress were modeled with a multinomial
regression model using sex, age and phase as covariates. A two-step cluster analysis (hierarchical and a k-means) was used to
determine the number of patterns of stress variation.
Results: In 730 participants (n= 730), the mean perceived stress rating was 3.4 (standard deviation = + 2.4) and the median was 3.0
(interquartile range = 3.0). The odds of high perceived stress scores increased in P2 and to a lesser extent in P1 relative to P0
(p<0.0001), in females relative to males, and decreased with age (p<0.05). Cluster analysis uncovered 6 dominant patterns of stress
variation. Although P2 had the highest odds of elevated perceived stress scores in the regression model, results of the cluster analysis
indicate that this is only true for 3 clusters of participants (cluster 1: n=205, cluster 3: n=78 and cluster 6: n=156). Other interesting
and distinct patterns were seen in clusters 2 (n=79), 4 (n=136) and 5 (n=75).
Conclusion: Although on an aggregate level perceived stress peaks during the pain phase, in individuals there appear to be 6 distinct
dominant patterns of stress variation across the migraine cycle. A better understanding of how stress and other related factors vary
across the migraine cycle in individuals may allow for insights into disease biology and facilitate targeted individualized treatment
plans in the future.