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:

Challenges of Virtual Testing in Statistical Quality Control of Railway Ballast
Authors: Vera Hofer (Department of Statistics, University of Graz), Holger Bach (Petromodel Ltd., Reykjavik)
Primary area of focus / application: Process
Secondary area of focus / application: Quality
Keywords: Change detection, Novelty detection, Statistical quality control, Machine learning
Submitted at 19Jun2014 09:26 by Vera Hofer
Accepted
are statistically estimated based on geometric and spectrographic features. This procedure is referred to as virtual testing. However, replacing manual testing by virtual testing requires that distributions on which the prediction model is based remain unchanged. However, there is no guarantee that, for example, new rock types that were not included in the training phase emerge. Thus, statistical monitoring of samples from daily production requires a novelty detection step to guarantee a high prediction performance quality. 
Estimating Time to Failure Distribution by Functional Data Analysis
Authors: Kamyar Sabrilaghaie (Iran University of Science and Technology), Rassoul Noorossana (Iran University of Science and Technology)
Primary area of focus / application: Reliability
Keywords: Reliability, Degradation measure, Functional Data Analysis, Time to failure
Submitted at 20Jun2014 01:12 by Kamyar Sabrilaghaie
Accepted

Minimum Volume Confidence Intervals under Prior Information for the Mean of a Poisson Distribution
Authors: Kristina Lurz (prognostica GmbH), Rainer Göb (University of Würzburg)
Primary area of focus / application: Reliability
Keywords: Confidence interval, Poisson distribution, Prior information, Count data
Submitted at 20Jun2014 10:24 by Kristina Krebs
Accepted
Göb, R., & Lurz, K. (2014). Design and analysis of shortest twosided confidence intervals for a probability under prior information. Metrika, vol. 77, no. 3, pp. 389413. 
A Study of the Copula Parameter Impact on Optimal Design of Experiments for Copula Models
Authors: Elisa Perrone (IFAS  Johannes Kepler University of Linz)
Primary area of focus / application: Design and analysis of experiments
Keywords: Design of Experiments, Copulas, Fisher information matrix, Equivalence theorem, Stochastic dependence
Submitted at 20Jun2014 12:45 by Elisa Perrone
Accepted

Comparison of Different Approaches for the Prediction of Sugar Content in Whole Port Wine Grape Berries Using Hyperspectral Imaging
Authors: Véronique Gomes (CITABCentre for the Research and Technology of AgroEnvironmental and Biological Sciences, Universidade de TrásosMontes e Alto Douro), Armando Fernandes (CITABCentre for the Research and Technology of AgroEnvironmental and Biological Sciences, Universidade de TrásosMontes e Alto Douro; Center of Intelligent Systems, IDMEC/LAETA, Instituto Superior Técnico, Universidade de Lisboa), Arlete Faia (IBB–Institute for Biotechnology and Bioengineering, Centre of Genomics and Biotechnology, Universidade de TrásosMontes e Alto Douro), Pedro MeloPinto (CITABCentre for the Research and Technology of AgroEnvironmental and Biological Sciences, Universidade de TrásosMontes e Alto Douro; Departamento de Engenharias, Escola de Ciências e Tecnologia, Universidade de TrásosMontes e Alto Douro)
Primary area of focus / application: Quality
Secondary area of focus / application: Modelling
Keywords: Prediction, Hyperspectral imaging, PLSR, Neural networks, Grapes berries

Bayesian Local Kriging
Authors: Luc Pronzato (CNRS), Joaõ Rendas (CNRS)
Primary area of focus / application: Design and analysis of experiments
Secondary area of focus / application: Modelling
Keywords: Gaussian process, Kriging, Prediction, Interpolation, Bayesian estimation
Submitted at 20Jun2014 14:41 by Luc Pronzato
Accepted
We propose a localkriging approach that faces the two difficulties above. First, we consider a different local model at each point $t$ where prediction is required, which allows us to account for non stationarity. Second, for each prediction site $t$ we consider a finite set of $L$ localized correlation functions $C_{\ellt}$, $\ell=1,\ldots,L$, and a local model $Z_{s(t)}$ with correlation function $C_{s(t)t}$ such that $s(t)=\ell$ with some probability $w^\ell(t)$. Starting with some prior weights $w^\ell_0$, we can then update them into $w^\ell_n$ after $n$ observations $\zb_n=(Z(x_1),\ldots,Z(x_n))\TT$ have been collected, and hence construct a Bayesian predictor $x\in\SX\longmapsto\hat z_n(xt)$ based on the $L$ local models. As shown below, due to the dependence of the $w^\ell_n$ in $\zb_n$, this predictor depends non linearly in $\zb_n$. By construction, $\hat z_n(xt)$ is only valid locally, for $x$ close to $t$, but the predictor $t\in\SX \longmapsto \hat z_n(t)=\hat z_n(tt)$, which we call Bayesian local kriging predictor, inherits continuity and interpolating properties from the properties of the correlation functions $C_{\ellt}$. This prediction and its posterior squared error can be constructed explicitly when we assume a linear parametric trend $\gb\TT(x)\beta$, with $\gb(\cdot)$ a known vector of functions (the usual framework for universal kriging), and a hierarchical prior $\beta\ms^2 \sim \SN(\beta_0,\ms^2 \Vb_0)$ and $\ms^2\sim$ inverse chisquared, common to the $L$ models. Various examples, with and without nonstationarity, will be presented to compare the performance of this localkriging method with that of universal kriging with maximum likelihood estimation of covariance parameters.