ENBIS: European Network for Business and Industrial Statistics
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ENBIS18 in Nancy
2 – 25 September 2018; Ecoles des Mines, Nancy (France) Abstract submission: 20 December 2017 – 4 June 2018The following abstracts have been accepted for this event:

Statistical Engineering: An Idea Whose Time Has Come?
Authors: Roger Hoerl (Union College)
Primary area of focus / application: Other: Statistical Engineering
Keywords: Engineering, Problem solving, Complex problems, Unstructured problems
Submitted at 29May2018 16:13 by Roger Hoerl
Accepted

A New ISO Standard for Isolated Lot Inspection by Variables Sampling
Authors: Rainer Göb (University of Wuerzburg)
Primary area of focus / application: Other: Sampling
Secondary area of focus / application: Quality
Keywords: ISO, Isolated lot inspection, Variables sampling, Design principles
Submitted at 29May2018 16:44 by Rainer Göb
Accepted
ISO 3951 is a multipart series of standards for variables sampling where the proportion nonconforming is determined as the probability of a normally distributed measurement falling outside a specification range.
ISO 2859 includes the process oriented attributes sampling standard 28591 and the isolated lot oriented standard 28592. ISO 3951 contains only the process oriented part, namely 39511 which is a descendent of the US military standard MILSTD414. For some times stakeholders particularly from the pharmaceutical and food industry have been requesting a variables sampling standard for isolated lot inpection. A draft for such a standard has recently been developed by the responsible technical committee TC 69 "Application of statistical methods'' at ISO. The talk outlines the new standard and explains the technical background and design principles, in particular technical details of matching sampling plans between the existing attributes sampling standard 28592 and the new variables sampling standard. 
Short OneSided Confidence Intervals for a Proportion
Authors: Jens Bischoff (University of Wuerzburg), Rainer Göb (University of Wuerzburg)
Primary area of focus / application: Other: Sampling
Secondary area of focus / application: Quality
Keywords: Onesided confidence intervals, Average coverage, Local restrictions, Prior information, Frequentistic

ARIMA Time Series Analysis of Nano Exposure Measurements
Authors: Wouter Fransman (TNO)
Primary area of focus / application: Other: statistical methods in industrial hygiene
Keywords: Time series, Nanomaterials, Exposure, ARIMA
Submitted at 30May2018 11:42 by Wouter Fransman
Accepted

Choice of Number of Whole Plots and Number of Runs in the Design of SplitPlot Experiments
Authors: Jacqueline Asscher (Kinneret College)
Primary area of focus / application: Design and analysis of experiments
Secondary area of focus / application: Consulting
Keywords: Splitplot, DOE, Consulting, Components of variance
Submitted at 30May2018 21:11 by Jacqueline Asscher
Accepted
While splitplot designs are familiar to experts in Design of Experiments (DOE), they are typically not fully understood by practitioners or clients, although they may be very attractive due to the lure of savings and convenience.
A key decision in the design of a splitplot experiment is the choice of how many whole plots and how many runs to include. The issues that arise when this decision is to be made are defined and discussed here.
Before considering the statistical properties of the design, the following questions are addressed: How do we calculate how this choice affects savings in time and/or money? How do we present the calculations? How do we clarify and display the design options to the owner of the experiment?
The variance of the effects depends on the number of whole plots and runs and on the size of the two components of variance, the variation between whole plots and the variation between runs within whole plots. The power of the tests to identify active effects depends on the size of the variance of the effects, the size of the effects, and on our ability to estimate the two components of variance. The latter is also determined by the number of whole plots and runs.
Questions that arise here include: What happens if I assume that the between plot variation is relatively small, or relatively large? What happens if I don’t know? What happens when the estimation of certain effects is of critical importance?
Other considerations in the choice of how many whole plots and how many runs to include are the proportion of factors that can only be changed between whole plots and the choice of model that is to be fitted.
Finally, the relationship between all of the issues involved is discussed. 
Autoencoding any Data through Kernel Autoencoders
Authors: Pierre Laforgue (Télécom ParisTech)
Primary area of focus / application: Modelling
Keywords: Kernel methods, Autoencoders, Operatorvalued kernels, Representation learning