ENBIS: European Network for Business and Industrial Statistics
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ENBIS14 in Linz
21 – 25 September 2014; Johannes Kepler University, Linz, Austria Abstract submission: 23 January – 22 June 2014The following abstracts have been accepted for this event:

Diagnostic Quality Problem Solving
Authors: Jeroen de Mast (University of Amsterdam)
Primary area of focus / application: Quality
Secondary area of focus / application: Six Sigma
Keywords: Diagnosis, DMAIC, Problem solving, Rootcause analysis
Submitted at 31Mar2014 14:52 by Jeroen de Mast
Accepted
The framework offers a scientific basis for studying and evaluating problem solving methodologies such as Six Sigma’s DMAIC model, Kepner and Tregoe’s Problem Analysis method, and Shainin’s system. For the practitioner, the framework clarifies the rationale for many problem solving techniques offered in courses and textbooks. We also offer indications and contraindications when techniques are promising, and demonstrate how they fit together in a coherent strategy. 
Statistical Evaluation of Binary Tests
Authors: Thomas Akkerhuis (IBIS UvA), Jeroen de Mast (IBIS UvA)
Primary area of focus / application: Metrology & measurement systems analysis
Keywords: Binary measurement, Binary MSA, MSA, Latent measurand, Gold standard unavailable, False acceptance probability, False reject probability
Submitted at 31Mar2014 15:21 by Thomas Akkerhuis
Accepted
The first challenge is dealt with by latent variable modeling. The varying probabilities of false acceptance and false rejection are accounted for by assigning parts a continuous condition that the inspections aim to reflect. To deal with the low prevalence of rejections, we propose an intricate sampling strategy, which combines items sampled from multiple sources (total items population, stream of rejected items, and historical data about rejections). 
Appointment Scheduling in Healthcare
Authors: Alex Kuiper (IBIS UvA)
Primary area of focus / application: Modelling
Keywords: Appointment scheduling, Utility function, Stochastic modeling, Healthcare
Submitted at 31Mar2014 16:52 by Alex Kuiper
Accepted
We study the problem in a wider context to incorporate flexibility in the actual service time distribution. Our approach is particularly valuable for practitioners who experience variation in the service times, e.g., healthcare situations, such as a doctor or dentist seeing patients. We give explicit statistics for the optimal schedule and show how it outperforms other commonlyused scheduling approaches. 
Sparse Bayesian Modelling with Spike and Slab Priors
Authors: Helga Wagner (Johannes Kepler University)
Primary area of focus / application: Modelling
Keywords: Categorial covariates, Effect fusion, MCMC, Sparsity
Submitted at 2Apr2014 11:03 by Helga Wagner
Accepted
In a Bayesian approach, variable selection relies on appropriate prior distributions on the effects subject to selection. Such priors are usually specified as mixtures of a spike and a slab component: the spike is centered at zero with a very small variance and the slab is flat. The finite mixture structure allows classification of effects as (practically) zero or nonzero.
Spike and slab priors have been employed to achieve sparsity in more complex models, e.g. random effects or state space models. This talk reviews Bayesian modelling with spike and slab priors in various model classes and discusses extensions to achieve a sparse representation of the effect of categorial covariates. As the effect of a categorial covariate with k+1 categories is modelled by a set of k regression coefficients  one for each covariate level except the baseline category  sparsity is achieved whenever the effect of the categorial predictor can be represented by fewer regression effects. We show, how fusion of levels with essentially the same effect is feasible by choosing appropriate prior distributions. For all models, Bayesian inference relies on MCMC methods. Performance of these methods will be illustrated on simulated as well as real data. 
IOptimal Mixture Designs
Authors: Peter Goos (Antwerp University, Leuven University), Bradley Jones (Antwerp Univeristy, SAS Institut), Utami Syafitri (Antwerp University, Bogor Agricultural Univeristy Indonesia)
Primary area of focus / application: Design and analysis of experiments
Secondary area of focus / application: Design and analysis of experiments
Keywords: Mixture experiment, Continuous mixtureexperiment, Doptimality, Ioptimality

Bayesian Effect Fusion for Categorial Predictors
Authors: Daniela Pauger (Johannes Kepler University)
Primary area of focus / application: Modelling
Keywords: Categorial predictors, Spike and slab prior distribution, Sparse modelling, Dummy coding
Submitted at 10Apr2014 14:26 by Daniela Pauger
Accepted
In a Bayesian approach sparsity can be achieved by choosing appropriate prior distributions, e.g. spike and slab prior distributions. These priors are mixtures of two components: the spike is centred at zero with very small variance and the slab is comparably flat. The finite mixture structure allows classification of effects as (practically) zero and nonzero. These spike and slab prior distributions are very popular for Bayesian variable selection.
Our aim is to extend the methods to allow variable selection and effect fusion for ordered as well as unordered categorial predictors. We demonstrate the developed methods using extensive simulation studies and real data.