ENBIS Spring Meeting 2019 in Barcelona

13 – 14 June 2019 Abstract submission: 15 January – 20 May 2019

My abstracts

 

The following abstracts have been accepted for this event:

  • 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
    Statistical Engineering is nothing new. I have used Statistical Methods within Engineering for 30 years. So it is not new. My presentation will provide examples of the practical application of Statistical Engineering and demonstrate a structure for the application of Statistical Engineering which I have used over the last 30 years.

    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'?
  • Statistical Functions for Clinical Trials

    Authors: Ismail Abbas (Universitat Politècnica de Catalunya)
    Primary area of focus / application: Modelling
    Keywords: Modeling, Simulation, Optimization, Clinical trials
    Submitted at 28-Feb-2019 13:26 by Ismail Abbas
    Accepted
    Clinical trials are multidisciplinary research experiments, costly, and the benefits of its health and economic effects often are uncertain, and therefore the resulting expected net benefits becomes a key question. Modeling and simulation is a computational technique which is able to aid structuring a trial mathematically and running it by computer simulations to generate data according to specified variables and to the main purpose of a trial. In this presentation, some statistical functions are suggested that are able to summarize the expected net benefits resulting from a real or a computational clinical trial. Then, these functions can be used for a variety of targets, such as confirming if and how likely one treatment is better than another from the view of cost-effectiveness analysis and expected net benefits, making different type of hypothesis testing, showing how worthy a trial can be and optimizing purposes under some statistic and economic aspects of a trial design. Finally, one example will be presented to show how and the strengths of using the proposed statistical functions, discuss limitations and other possible applications.
  • 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
    Statistical Engineering seeks to solve tough problems with an holistic, multidisciplinary approach. This is a challenging agenda, requiring the help of software that can support learning, and encapsulate and make accessible the knowledge that results. This presentation aims to articulate the capabilities that software needs to do this adequately, and in so doing, suggests why new patterns of use may be required. These might be provided by more disciplined and consistent use of existing applications, or by software that does not yet exist. To make the scope manageable, we will focus on empirical model building using data, an important part of Statistical Engineering.
  • 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
    Understanding the spending pattern of European funds is an issue of primary importance as to evaluate their effectiveness in generating benefits at variable spatial and time scales. This especially applies to funds that are remitted at the regional level, such as the ESIF (European Structural and Investment Funds), which represents the main financial tool the European Commission resorts to foster its cohesion policy. However, producing meaningful inference on the local effects yield by these funds faces an inevitable issue of time lagging: The payments from the European Commission are remitted only after the incurred expenses have been invoiced. Precisely, the only official figures available report on the dates when the EU payment were remitted to the local authorities. These figures may be delayed from the actual year on which expenditures were invoiced by the final beneficiaries to the local authorities. These figures are typically not available. Therefore, producing econometric inference on the benefits these ESIF locally produce on the ground may be cumbersome due to this very time lag.

    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
    Submitted at 2-May-2019 17:26 by Alessandro Di Bucchianico
    Accepted
    Calculation of Remaining Useful Life (RUL) is an important new trend in maintenance. It is an essential element to go from diagnostic analytics to predictive or rather prescriptive analytics, to use data science terminology. In Industry 4.0 settings, we have to determine RUL in data rich environments with an abundance of sensor values and degradation indicators but few failures. In such settings machine learning techniques may be useful to get predictions when complex relations between sensor values exist that make statistical modelling not feasible. However, machine learning techniques typically fail short of providing indications of uncertainty. To combine the best of two worlds, we propose an approach that combines machine learning and statistics. Recurrent neural networks are used to provides predictions from degradation values, and we use stochastic degradation models to add uncertainty to the prediction.

    The proposed approach to RUL prediction will be illustrated with an industrial use case within the EU Horizon 2020 project Prophesy.