ENBIS: European Network for Business and Industrial Statistics
Forgotten your password?
Not yet a member? Please register
ENBIS13 in Ankara
15 – 19 September 2013 Abstract submission: 5 February – 5 June 2013The following abstracts have been accepted for this event:

Modeling of Response Surfaces with Replicated Measures by Using Fuzzy Least Squares Regression and Switching Fuzzy CRegression
Authors: Özlem Türkşen (Ankara University), Nevin Güler (Mugla Sıtkı Koçman University), Ayşen Apaydın (Ankara University)
Primary area of focus / application: Other: Fuzzy
Keywords: multiresponse experiments, replicated measurement, Fuzzy Least Squares Regression (FLSR), Switching Fuzzy CRegression (SFCR)
Submitted at 29May2013 22:49 by Özlem Türkşen
Accepted

Improving Shewharttype Control Charts for Monitoring Multivariate Gaussian Process Variability: A Unified View from Generalized Variance to Loglikelihood Ratio Statistics
Authors: Emanuel Pimentel Barbosa (State University of Campinas  UNICAMP), Mario Antonio Gneri (State University of Campinas  UNICAMP), Ariane Meneguetti (State University of Campinas  UNICAMP)
Primary area of focus / application: Process
Keywords: Control Charts, Exact Control Limits, Generalized Variance, LogLikelihood Ratio, Multivariate Process, Variability Monitoring
Submitted at 29May2013 22:53 by Emanuel Pimentel Barbosa
Accepted
Although we can approximate well the S sampling distribution quantiles using mathematical tools such as CornishFisher (CF) expansion or Meijer Gfunctions (as we did in a previous paper), however, this statistic still has an important drawback: it does not detect all sorts of changes in the Sigma matrix (only detect the changes that alter Sigma).
In order to overcome this drawback, a solution is proposed here, initially, based on the inclusion of an auxiliary statistic/chart based on the trace(S), to be used jointly with our previous S control chart with CF corrected limits; the motivation for this is clear if we look to the likelihood. The trace(S) statistic quantiles (auxiliary chart limits),
with S in a proper standardized form, are obtained by heavy simulation, and one practical table of upperquantiles is provided, for different sample sizes n and process dimensions p.
Also, in an alternative second version of our proposed procedure, we consider these two statistics (S and trace(S)) packed together in just one statistic (chart): the LLR in proper standardized form, which gives not only an unified view of the problem, but also a practical and efficient monitoring tool for Sigma.
The LLR sampling quantiles (chart limits) are obtained in a similar way, by heavy (millions of samples) simulation using Wishart generators of Matlab or R, and a table of quantiles (upper limits) is provided for different n and p , and usual alpha risks of false alarm of 0.0027 and 0.0020. In order to illustrate the two operating versions of our proposed procedure for improving the S and LLR control charts, a couple of examples with real data are provided. 
Inverse Modeling to Estimate Methane Surface Emission with Optimization and Reduced Models: Application of Waste Landfill Plants
Authors: Mireille BattonHubert (Ecole Nationale supérieure des Mines), Mickael Binois (Ecole Nationale Supérieure des Mines), Espéran Padonou (Ecole Nationale Supérieure des Mines)
Primary area of focus / application: Modelling
Keywords: inverse approach, biogas emission, monitoring sampling, optimization, sensitivity analysis, least square regression
Submitted at 30May2013 09:45 by Mireille BattonHubert
Accepted
Consequently we develop an inverse method to estimate the flow (g/m2/s) using the monitoring of the CH4 air quality on the site The inverse approach uses a direct modeling of atmospheric dispersion (between source of emission and receptors) linked with an optimization approach to estimate the average flow of emission. Firstly the approach (method) was validated on simulated results on different scenarii (constant and fluctuant emissions) before being applied to real data. A second phase was dedicated to the evaluation of the uncertainty of the results.
This inverse approach is already up and running with fair good results. However, the simulation study is quite time consuming. As consequence further works are going on to:
 Reduce the dimension of the problem by using the correlations between the simulator’s outputs
 Model the inverse approach simulator by using a design of experiment based on kriging 
Improving Hospital Billing Processes for Reducing Costs of Billing Errors
Authors: Erdi Dasdemir (Hacettepe University, Department of Industrial Engineering), Murat Atalay (Hacettepe University, Department of Industrial Engineering), Macit Mete Oğuz (Hacettepe University, Department of Industrial Engineering), Volkan Bilgin (Hacettepe University, Department of Industrial Engineering), Mıurat Caner Testik (Hacettepe University, Department of Industrial Engineering), Guray Soydan (Hacettepe University Hospital)
Primary area of focus / application: Other: Special Session: Healthcare Systems Engineering
Keywords: Six Sigma, Process Improvement, Lean Hospital, Medical Billing Process, Billing Errors

Models for Nurse Rostering Problem
Authors: Banu YukselOzkaya (Hacettepe University), Murat Caner Testik (Hacettepe University)
Primary area of focus / application: Other: Health Care
Keywords: health care, nurse rosters, mathematical model, hard/soft constraint

Investigating the Performance of Bootstrapping Conic MARS (BCMARS) Method for Large Datasets
Authors: İnci Batmaz (Middle East Technical University), Ceyda Yazıcı (Middle East Technical University)
Primary area of focus / application: Mining
Keywords: CMARS, RandomX Bootstrap, FixedX Bootstrap, Wild Bootstrap
Submitted at 30May2013 13:48 by Ceyda YAZICI
Accepted