ENBIS: European Network for Business and Industrial Statistics
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ENBIS16 in Sheffield
11 – 15 September 2016; Sheffield Abstract submission: 20 March – 4 July 2016The following abstracts have been accepted for this event:

TreeBased Methods for (Life) Insurance and Some Considerations about Statistics and Actuarial Mathematics
Authors: Walter Olbricht (University of Bayreuth)
Primary area of focus / application: Business
Secondary area of focus / application: Mining
Keywords: Treebased methods, Life insurance, Specific data features, Internal structure of the data
Submitted at 28May2016 13:03 by Walter Olbricht
Accepted
The talk analyses this background and suggests treebased methods as an interesting statistical tool even for the classical field of life insurance. In particular a “hybrid” approach (using regression trees for a classification situation) is proposed. The main advantage of this approach is its ease of interpretability and its inherent transparency. 
A Bayesian SelfStarting Method for Online Monitoring of Phase I Data
Authors: Panagiotis Tsiamyrtzis (Athens University of Economics and Business), Konstantinos Bourazas (Athens University of Economics and Business), Dimitrios Kiagias (University of Sheffield)
Primary area of focus / application: Process
Secondary area of focus / application: Modelling
Keywords: Statistical Process Control and monitoring, Predictive distribution, Exponential family, Phase I outlier detection
In this work a Bayesian alternative is proposed, which utilizes the (usually) available prior distribution to provide a chart based on the predictive distribution of a future observable (Predictive Control Chart – PCC). It is selfstarting and performs online monitoring, right after the first observable becomes available. PCC will be presented in its most general form, allowing data of any (discrete or continuous) distribution as long as it is a member of the regular exponential family. We will also establish that PCC generalizes frequentist selfstarting methods. Simulations will examine its performance against the frequentist based methods and a real data set will illustrate its use. 
Bayesian Design of Experiments via Gaussian Process Emulation
Authors: David Woods (University of Southampton)
Primary area of focus / application: Design and analysis of experiments
Keywords: Computer experiments, Generalised linear models, High dimensional design, Nonlinear design, Smoothing
Submitted at 30May2016 11:43 by David Woods
Accepted
When designing experiments for nonlinear parametric models, finding a Bayesian optimal design is typically analytically intractable and often computational infeasible. The expected loss usually involves an intractable and high dimensional integral. We will present methodology for mitigating the computational expense of design through combining an application of Gaussian Process (GP) regression models with a cyclic descent (coordinate exchange) optimisation algorithm. We adopt a methodology from the field of computer experiments and build a GP emulator for the expected loss. The methods allow optimal designs to be found for previously infeasible problems. We will describe the methodology and demonstrate it on a variety of examples. 
Effects of Imperfect Testing on PresenceAbsence Sampling Plans
Authors: Edgar SantosFernández (Massey University), K. Govindaraju (Massey University), Geoff Jones (Massey University)
Primary area of focus / application: Quality
Secondary area of focus / application: Modelling
Keywords: Sampling inspection plan, Risk assessment, Measurement errors, Presenceabsence tests, Bayesian inference, Sensitivity and specificity

Bespoke Online App for Design of Experiments for a Research Robot
Authors: Winfried Theis (Shell Global Solutions International B.V.), Mark Brewer (Shell Global Solutions International B.V.), Axel Makurat (Shell Global Solutions International B.V.)
Primary area of focus / application: Design and analysis of experiments
Secondary area of focus / application: Consulting
Keywords: Design of Experiments, Mixture design, Space filling, Creating web applications, R Shiny
Submitted at 30May2016 15:47 by Winfried Theis
Accepted
In this talk we will explain the way how we solved the various potential restrictions on the design space but also will show how simple it was to create an online user interface with RShiny and demo it shortly. 
A Bayesian Model for Count Data Affected by False Positives and False Negatives
Authors: Francesca Pennecchi (Istituto Nazionale di Ricerca Metrologica  INRIM), Walter Bich (Istituto Nazionale di Ricerca Metrologica  INRIM), Cinzia Carota (Dipartimento di Economia e Statistica “Cognetti de Martiis”, Università degli Studi di Torino), Giancarlo D’Agostino (Istituto Nazionale di Ricerca Metrologica  INRIM), Marco Di Luzio (Istituto Nazionale di Ricerca Metrologica  INRIM), Consuelo R. Nava (Dipartimento di Economia e Statistica “Cognetti de Martiis”, Università degli Studi di Torino), Alessio Ricci (Dipartimento di Economia e Statistica “Cognetti de Martiis”, Università degli Studi di Torino)
Primary area of focus / application: Metrology & measurement systems analysis
Secondary area of focus / application: Modelling
Keywords: Count data, Bayesian inference, False positives, False negatives, Measurement uncertainty
Submitted at 30May2016 16:48 by Francesca Pennecchi
Accepted
In the present work, we address the same problem by using a hierarchical Bayesian model, which takes into account the probabilities of false positives and false negatives, inducing overestimates and underestimates of the measurand, i.e. the unknown count, respectively. Prior information on the measurand is introduced at the first level of the hierarchy, then we enrich the model by adding a second level where further information on such two probabilities is exploited.
Such a model is applied to a radioactivity measurement problem where the measurand is the number of radioactive atoms decaying in a definite counting time. Due to the dead time of the detection system  a standby period after the counting of each event which follows gamma photon detection and is required to process it  there is a nonzero probability that some events are not counted. The posterior probability density function of the measurand proves to be able to correct for the presence of such false negatives, providing a reliable estimate of the number of decayed atoms.