Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Vulnerability, Uncertainty, and Risk |
| Untertitel | Quantification, Mitigation, and Management - Proceedings of the 2nd International Conference on Vulnerability and Risk Analysis and Management, ICVRAM 2014 and the 6th International Symposium on Uncertainty Modeling and Analysis, ISUMA 2014 |
| Herausgeber/-innen | Jim W. Hall, Siu-Kui Au, Michael Beer |
| Herausgeber (Verlag) | American Society of Civil Engineers (ASCE) |
| Seiten | 370-377 |
| Seitenumfang | 8 |
| ISBN (elektronisch) | 9780784413609 |
| Publikationsstatus | Veröffentlicht - 2014 |
| Extern publiziert | Ja |
| Veranstaltung | 2nd International Conference on Vulnerability and Risk Analysis and Management, ICVRAM 2014 and the 6th International Symposium on Uncertainty Modeling and Analysis, ISUMA 2014 - Liverpool, Großbritannien / Vereinigtes Königreich Dauer: 13 Juli 2014 → 16 Juli 2014 |
Abstract
An analytical expression for the probability density function (PDF) of a random process power spectrum value at a given frequency is derived. The significance of the derived PDF relates to cases where incomplete process realizations are available for power spectrum estimation applications. Specifically, standard power spectrum estimation techniques subject to missing data-such as zero-padding and least squares regression-normally provide a deterministic estimate for the power spectrum value at a given frequency. In this regard, no information is provided concerning the uncertainty in the estimates. In this paper, relying on the assumptions of stationarity and ergodicity, and on minimum assumptions about the missing data, an explicit closed-form analytical expression for the PDF of the power spectrum value at each and every frequency is derived. In this manner, the uncertainty propagation-from the measured incomplete process realization in the time/space domain to its power spectrum estimate in the frequency domain-is efficiently quantified. Further, the derived results can, potentially, be used for assessing the performance of alternative power spectrum estimation techniques subject to missing data which provide a deterministic estimate for the power spectrum value at a specific frequency.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Sicherheit, Risiko, Zuverlässigkeit und Qualität
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Vulnerability, Uncertainty, and Risk: Quantification, Mitigation, and Management - Proceedings of the 2nd International Conference on Vulnerability and Risk Analysis and Management, ICVRAM 2014 and the 6th International Symposium on Uncertainty Modeling and Analysis, ISUMA 2014. Hrsg. / Jim W. Hall; Siu-Kui Au; Michael Beer. American Society of Civil Engineers (ASCE), 2014. S. 370-377.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Uncertainty Quantification in Power Spectrum Estimation of Stochastic Processes Subject to Missing Data
AU - Comerford, L.
AU - Kougioumtzoglou, I. A.
AU - Beer, M.
PY - 2014
Y1 - 2014
N2 - An analytical expression for the probability density function (PDF) of a random process power spectrum value at a given frequency is derived. The significance of the derived PDF relates to cases where incomplete process realizations are available for power spectrum estimation applications. Specifically, standard power spectrum estimation techniques subject to missing data-such as zero-padding and least squares regression-normally provide a deterministic estimate for the power spectrum value at a given frequency. In this regard, no information is provided concerning the uncertainty in the estimates. In this paper, relying on the assumptions of stationarity and ergodicity, and on minimum assumptions about the missing data, an explicit closed-form analytical expression for the PDF of the power spectrum value at each and every frequency is derived. In this manner, the uncertainty propagation-from the measured incomplete process realization in the time/space domain to its power spectrum estimate in the frequency domain-is efficiently quantified. Further, the derived results can, potentially, be used for assessing the performance of alternative power spectrum estimation techniques subject to missing data which provide a deterministic estimate for the power spectrum value at a specific frequency.
AB - An analytical expression for the probability density function (PDF) of a random process power spectrum value at a given frequency is derived. The significance of the derived PDF relates to cases where incomplete process realizations are available for power spectrum estimation applications. Specifically, standard power spectrum estimation techniques subject to missing data-such as zero-padding and least squares regression-normally provide a deterministic estimate for the power spectrum value at a given frequency. In this regard, no information is provided concerning the uncertainty in the estimates. In this paper, relying on the assumptions of stationarity and ergodicity, and on minimum assumptions about the missing data, an explicit closed-form analytical expression for the PDF of the power spectrum value at each and every frequency is derived. In this manner, the uncertainty propagation-from the measured incomplete process realization in the time/space domain to its power spectrum estimate in the frequency domain-is efficiently quantified. Further, the derived results can, potentially, be used for assessing the performance of alternative power spectrum estimation techniques subject to missing data which provide a deterministic estimate for the power spectrum value at a specific frequency.
UR - http://www.scopus.com/inward/record.url?scp=84933510334&partnerID=8YFLogxK
U2 - 10.1061/9780784413609.038
DO - 10.1061/9780784413609.038
M3 - Conference contribution
AN - SCOPUS:84933510334
SP - 370
EP - 377
BT - Vulnerability, Uncertainty, and Risk
A2 - Hall, Jim W.
A2 - Au, Siu-Kui
A2 - Beer, Michael
PB - American Society of Civil Engineers (ASCE)
T2 - 2nd International Conference on Vulnerability and Risk Analysis and Management, ICVRAM 2014 and the 6th International Symposium on Uncertainty Modeling and Analysis, ISUMA 2014
Y2 - 13 July 2014 through 16 July 2014
ER -