Details
| Original language | English |
|---|---|
| Title of host publication | VI Hotine-Marussi Symposium on Theoretical and Computational Geodesy - IAG Symposium |
| Pages | 232-237 |
| Number of pages | 6 |
| ISBN (electronic) | 978-3-540-74584-6 |
| Publication status | Published - 2008 |
| Event | IAG Symposium - 6th Hotine-Marussi Symposium on Theoretical and Computational Geodesy - Wuhan, China Duration: 29 May 2006 → 2 Jun 2006 |
Publication series
| Name | International Association of Geodesy Symposia |
|---|---|
| Volume | 132 |
| ISSN (Print) | 0939-9585 |
Abstract
The total uncertainty budget of geodetic data usually comprises two main types of uncertainty: random variability which reflects uncontrollable effects during observation and data processing, and imprecision which is due to remaining systematic errors between data and model. Whereas random variability can be treated by means of stochastics, it is more adequate to model imprecision using Fuzzy-theory. Hence, it is necessary to extend the classical techniques of geodetic data analysis such as parameter estimation and statistical hypothesis testing in a suitable way in order to take imprecision into account. The study focuses on imprecise vector data and on the consistent extension of a multidimensional hypothesis test which is based on a quadratic form. Within the considered approach it is also possible to introduce fuzzy regions of acceptance and rejection in order to model linguistic uncertainties. For the final decision the crisp degree of rejectability for the null hypothesis is computed. Whereas in the one-dimensional case this is straightforward, in the multidimensional case the so-called α-cut optimization technique has to be applied. The global test in outlier detection and the congruence test of static deformation analysis are considered as application examples.
Keywords
- α-cut optimization, congruence test, fuzzy data analysis, global test, Imprecise data, multidimensional hypothesis test, outlier detection
ASJC Scopus subject areas
- Earth and Planetary Sciences(all)
- Computers in Earth Sciences
- Earth and Planetary Sciences(all)
- Geophysics
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
VI Hotine-Marussi Symposium on Theoretical and Computational Geodesy - IAG Symposium. 2008. p. 232-237 (International Association of Geodesy Symposia; Vol. 132).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Multidimensional statistical tests for imprecise data
AU - Kutterer, H.
AU - Neumann, I.
PY - 2008
Y1 - 2008
N2 - The total uncertainty budget of geodetic data usually comprises two main types of uncertainty: random variability which reflects uncontrollable effects during observation and data processing, and imprecision which is due to remaining systematic errors between data and model. Whereas random variability can be treated by means of stochastics, it is more adequate to model imprecision using Fuzzy-theory. Hence, it is necessary to extend the classical techniques of geodetic data analysis such as parameter estimation and statistical hypothesis testing in a suitable way in order to take imprecision into account. The study focuses on imprecise vector data and on the consistent extension of a multidimensional hypothesis test which is based on a quadratic form. Within the considered approach it is also possible to introduce fuzzy regions of acceptance and rejection in order to model linguistic uncertainties. For the final decision the crisp degree of rejectability for the null hypothesis is computed. Whereas in the one-dimensional case this is straightforward, in the multidimensional case the so-called α-cut optimization technique has to be applied. The global test in outlier detection and the congruence test of static deformation analysis are considered as application examples.
AB - The total uncertainty budget of geodetic data usually comprises two main types of uncertainty: random variability which reflects uncontrollable effects during observation and data processing, and imprecision which is due to remaining systematic errors between data and model. Whereas random variability can be treated by means of stochastics, it is more adequate to model imprecision using Fuzzy-theory. Hence, it is necessary to extend the classical techniques of geodetic data analysis such as parameter estimation and statistical hypothesis testing in a suitable way in order to take imprecision into account. The study focuses on imprecise vector data and on the consistent extension of a multidimensional hypothesis test which is based on a quadratic form. Within the considered approach it is also possible to introduce fuzzy regions of acceptance and rejection in order to model linguistic uncertainties. For the final decision the crisp degree of rejectability for the null hypothesis is computed. Whereas in the one-dimensional case this is straightforward, in the multidimensional case the so-called α-cut optimization technique has to be applied. The global test in outlier detection and the congruence test of static deformation analysis are considered as application examples.
KW - α-cut optimization
KW - congruence test
KW - fuzzy data analysis
KW - global test
KW - Imprecise data
KW - multidimensional hypothesis test
KW - outlier detection
UR - http://www.scopus.com/inward/record.url?scp=84884304508&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-74584-6_37
DO - 10.1007/978-3-540-74584-6_37
M3 - Conference contribution
AN - SCOPUS:84884304508
SN - 9783540745839
T3 - International Association of Geodesy Symposia
SP - 232
EP - 237
BT - VI Hotine-Marussi Symposium on Theoretical and Computational Geodesy - IAG Symposium
T2 - IAG Symposium - 6th Hotine-Marussi Symposium on Theoretical and Computational Geodesy
Y2 - 29 May 2006 through 2 June 2006
ER -