Hatzfeld, Christian ; Bilz, Johannes ; Schlemmer, Sascha ; Adolf, Jan-Eric ; Gu, Yangyang ; Elgner, Steffen ; Kupnik, Mario (2017)
Residual Analysis to Compare Measurement Series’ Differences with Significance and Size Measures.
IEEE SENSORS. Glasgow
Conference or Workshop Item, Bibliographie
Abstract
Current methods to compare measurement series are based on the comparison of multiple measurement points or fit parameters. These approaches depend on the quality of the fit and are prone to type I error accumulation, when standard statistical tests are used for evaluation. In this work, we present an approach based on a two-sample Kolmogorov-Smirnov test of residuals of data series to a fit in order to assess measurement series differences. This approach does neither rely on physical accurate model functions nor equal sample sizes or variances. It provides comparable measures for data differences as well as a reliability measure of the assessment. It is validated with a Monte Carlo simulation. Applications include the comparison of measurement series with diverse sampling rates or the comparison of measurements without clearly distinguishable properties.
| Item Type: | Conference or Workshop Item |
|---|---|
| Erschienen: | 2017 |
| Creators: | Hatzfeld, Christian ; Bilz, Johannes ; Schlemmer, Sascha ; Adolf, Jan-Eric ; Gu, Yangyang ; Elgner, Steffen ; Kupnik, Mario |
| Type of entry: | Bibliographie |
| Title: | Residual Analysis to Compare Measurement Series’ Differences with Significance and Size Measures |
| Language: | English |
| Date: | 29 October 2017 |
| Event Title: | IEEE SENSORS |
| Event Location: | Glasgow |
| Abstract: | Current methods to compare measurement series are based on the comparison of multiple measurement points or fit parameters. These approaches depend on the quality of the fit and are prone to type I error accumulation, when standard statistical tests are used for evaluation. In this work, we present an approach based on a two-sample Kolmogorov-Smirnov test of residuals of data series to a fit in order to assess measurement series differences. This approach does neither rely on physical accurate model functions nor equal sample sizes or variances. It provides comparable measures for data differences as well as a reliability measure of the assessment. It is validated with a Monte Carlo simulation. Applications include the comparison of measurement series with diverse sampling rates or the comparison of measurements without clearly distinguishable properties. |
| Divisions: | 18 Department of Electrical Engineering and Information Technology > Institute for Electromechanical Design (dissolved 18.12.2018) 18 Department of Electrical Engineering and Information Technology > Microtechnology and Electromechanical Systems 18 Department of Electrical Engineering and Information Technology > Measurement and Sensor Technology 18 Department of Electrical Engineering and Information Technology |
| Date Deposited: | 07 Nov 2017 08:41 |
| Last Modified: | 07 Nov 2017 08:41 |
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