ENBIS: European Network for Business and Industrial Statistics
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ENBIS17 in Naples
9 – 14 September 2017; Naples (Italy) Abstract submission: 21 November 2016 – 10 May 2017The following abstracts have been accepted for this event:

Adversarial Hypothesis Testing
Authors: Fabrizio Ruggeri (CNR IMATI), David Rios Insua (CSIC ICMAT), Refik Soyer (George Washington University), Jorge González Ortega (CSIC ICMAT)
Primary area of focus / application: Other: Advances in Adversarial Risk Analysis
Keywords: Adversarial risk analysis, Hypothesis testing, Game theory, Decision analysis
Submitted at 25Jan2017 17:41 by Fabrizio Ruggeri
Accepted

Estimating Mixed Logit Models
Authors: Martina Vandebroek (KU Leuven), Deniz Akinc (KU Leuven)
Primary area of focus / application:
Secondary area of focus / application: Other: Design of Experiment for Product Quality and Sustainability in AgriFood Systems
Keywords: Discrete choice model, Mixed Logit model, Simulated Maximum Likelihood, Hierarchical Bayesian estimation
Submitted at 27Jan2017 14:48 by Martina Vandebroek
Accepted

Modeling Shrimp Growth in Freshwater
Authors: Susana Vegas (Universidad de Piura), Valeria Quevedo (Universidad de Piura/ Virginia Tech), Geoff Vining (Virginia Tech)
Primary area of focus / application: Other: DOE and statistical process monitoring in South America
Keywords: Process control, Nonlinear model, Control chart, Twostage model
In the first stage, we use a conceptual Gompertz growth model
logW = θ_1θ_2 e^(θ_3 time)
where θ_1 is the asymptotic average logweight of adult shrimp, θ_3 is the growth rate constant, and θ_1θ_2 is the average initial logweight to predict the shrimp logweight based on time. Using data from five harvesting campaigns, our analysis show that the best estimates for and can be computed using data from previous campaigns, and for is based on the average logweight from week 1 from the current campaign.
To explain the variability left unexplained from the first stage, we fit a multiple linear regression model with the nonlinear model residuals as the response, and average food per shrimp, aeration, and water parameters as predictors. To analyze the model efficiency, we estimate the shrimp weight by going backwards to the original scale. We show that the twostage nonlinear model satisfies the model assumptions better than a onestage MLR model.
The growth model from the first stage can be used to monitor the process for new campaigns by using a control chart using data from previous campaigns for the control limits. The secondstage regression analysis can be used to suggest corrections during the campaigns. 
A Tutorial on an Iterative Approach for Generating Shewhart Control Limits
Authors: Valeria Quevedo (Universidad de Piura), Susana Vegas (Universidad de Piura), Geoff Vining (Virginia Tech)
Primary area of focus / application: Other: ASQ international journal session
Keywords: Control charts, Control limits, Statistical process control, Shewhart charts
The typical presentation of Phase I is a preset number of rational subgroups. Such an approach fails to address the tension that exists between having enough information to generate reliable control limits and being able to start active control of the process. A key point is that control charts allow the engineer to change the process generating the data, unlike post hoc hypothesis tests. Vining (1998; 2009) outline an iterative approach that he developed in the early 1980s when he was employed by the FaberCastell Corp. The purpose of this iterative procedure is to construct the chart so that it generates a better model of the incontrol process.
This article discusses the differences between Phase I and Phase II control charts and the impact of the number of rational subgroups in the Phase I study upon the quality of the resulting control limits. It then presents two brief case studies that illustrate the iterative approach for the basic Shewhart Xbar and R charts. 
How to Evaluate Uncertainty of Categorical Measurements?
Authors: Emil Bashkansky (ORT Braude College of Engineering), Tamar Gadrich (ORT Braude College of Engineering)
Primary area of focus / application: Other: Sampling
Keywords: Classification (confusion) matrix, Acceptance sampling, Categorical scale, Bayesian approach

Assessing Assessors (by Means of Binary Test Design and Analysis)
Authors: Emil Bashkansky (ORT Braude College of Engineering), Vladimir Turetsky (ORT Braude College of Engineering)
Primary area of focus / application: Other: Reliability of Subjective Measurement Systems
Keywords: Assessors, Proficiency test (PT), Binary data, Ability and difficulty, Item response model, Maximal Likelihood estimation
We consider the case where the assessors under proficiency evaluation conduct the same test consisting of a set of binary test items presenting different, but unknown beforehand levels of difficulty (known or unknown beforehand). When trying to detect a particular property of objects under test, we need to evaluate/compare both the intrinsic abilities of the participating assessors and the level of difficulty of these objects/test items (if they are unknown beforehand). We assume that the responses to different test items do not affect one another and discuss how to get and interpret the most likely estimates/scores. The contribution of placebo in the test effectiveness will also be discussed. The method is illustrated by the example of 28 medical laboratories proficiency testing. We propose, as a criterion for screening for ‘‘bad’’ appraisers, to use the probability of successful detection of the studied property by test item/s having a certain, predetermined or defined posteriori level of difficulty.