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Maxime Deregnaucourt, M. Kozek:
"Robust Quality Analysis Using Coarsely Discretized Measurements";
Talk: 4th International Conference on MANUFACTURING ENGINEERING, QUALITY and PRODUCTION SYSTEMS (MEQAPS '11), Barcelona, Spain; 2011-07-15 - 2011-07-17; in: "Proceedings of the 4th International Conference on MANUFACTURING ENGINEERING, QUALITY and PRODUCTION SYSTEMS (MEQAPS '11)", (2011), ISBN: 978-1-61804-033-6; Paper ID MEQAPS-38, 6 pages.

In many industrial quality analysis applications data samples have small size, are coarsely discretized,
and potentially contain outliers. In order to ensure optimal decisions based on that data, a robust method for
parameter estimation and hypothesis testing is necessary. This paper presents a modified discretized Kolmogorov-
Smirnov statistic, which allows for theoretically optimal and effective treatment of such data. The proposed method
is compared to existing methods and its superiority is demonstrated by a sample from an industrial process and a
Monte Carlo simulation.

Statistical hypothesis test, censured data, outlier-robust, small sample size, goodness of fit, Kolmogorov-Smirnov distance, likelihood, distribution

Project Head Stefan Jakubek:
Christian Doppler Labor für Modellbasierte Kalibriermethoden