Application of Confidence Regions to Ice Ridge Keel Data Statistical Assessment

Petr Zvyagin, Jaakko Heinonen

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

1 Citation (Scopus)

Abstract

Sets of measurements of underwater ridge parts usually contain a limited amount of data. Outcomes need to be made while relying on small sample sizes. In this event, the chance of making inaccurate estimations increases.

This paper proposes to use stochastic confidence regions in the estimation of the unknown parameters of keel depths. A model for a random variable with a lognormal distribution for keel depths is assumed. Regions for the mean and standard deviation of keel depths are obtained from Mood’s and minimum-area confidence regions for parameters of the normally distributed random variable. Conservative safety probability of non-exceeding the critical keel depth in one random interaction of the ridge with structure is estimated.

An algorithm for statistically assessment of ice ridge keel data by means of confidence region building is here offered. The assessment of a set of ridge keel depths for the Gulf of Bothnia (Baltic Sea) is performed.
Original languageEnglish
Title of host publicationASME 2017 36th International Conference on Ocean, Offshore and Arctic Engineering
Subtitle of host publicationPolar and Arctic Sciences and Technology, Petroleum Technology
PublisherAmerican Society of Mechanical Engineers ASME
Number of pages6
Volume8
ISBN (Electronic)9780791857762
ISBN (Print)978-0-7918-5776-2
DOIs
Publication statusPublished - 1 Jan 2017
MoE publication typeA4 Article in a conference publication
Event36th International Conference on Ocean, Offshore and Arctic Engineering, OMAE2017 - Trondheim, Norway
Duration: 25 Jun 201730 Jun 2017

Conference

Conference36th International Conference on Ocean, Offshore and Arctic Engineering, OMAE2017
Abbreviated titleOMAE2017
CountryNorway
CityTrondheim
Period25/06/1730/06/17

Fingerprint

Random variables
Ice

Keywords

  • ice ridges
  • ridge keel
  • joint confidence region
  • lognormal distribution

Cite this

Zvyagin, P., & Heinonen, J. (2017). Application of Confidence Regions to Ice Ridge Keel Data Statistical Assessment. In ASME 2017 36th International Conference on Ocean, Offshore and Arctic Engineering: Polar and Arctic Sciences and Technology, Petroleum Technology (Vol. 8). [OMAE2017-62253] American Society of Mechanical Engineers ASME. https://doi.org/10.1115/OMAE2017-62253
Zvyagin, Petr ; Heinonen, Jaakko. / Application of Confidence Regions to Ice Ridge Keel Data Statistical Assessment. ASME 2017 36th International Conference on Ocean, Offshore and Arctic Engineering: Polar and Arctic Sciences and Technology, Petroleum Technology. Vol. 8 American Society of Mechanical Engineers ASME, 2017.
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Zvyagin, P & Heinonen, J 2017, Application of Confidence Regions to Ice Ridge Keel Data Statistical Assessment. in ASME 2017 36th International Conference on Ocean, Offshore and Arctic Engineering: Polar and Arctic Sciences and Technology, Petroleum Technology. vol. 8, OMAE2017-62253, American Society of Mechanical Engineers ASME, 36th International Conference on Ocean, Offshore and Arctic Engineering, OMAE2017, Trondheim, Norway, 25/06/17. https://doi.org/10.1115/OMAE2017-62253

Application of Confidence Regions to Ice Ridge Keel Data Statistical Assessment. / Zvyagin, Petr; Heinonen, Jaakko.

ASME 2017 36th International Conference on Ocean, Offshore and Arctic Engineering: Polar and Arctic Sciences and Technology, Petroleum Technology. Vol. 8 American Society of Mechanical Engineers ASME, 2017. OMAE2017-62253.

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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Zvyagin P, Heinonen J. Application of Confidence Regions to Ice Ridge Keel Data Statistical Assessment. In ASME 2017 36th International Conference on Ocean, Offshore and Arctic Engineering: Polar and Arctic Sciences and Technology, Petroleum Technology. Vol. 8. American Society of Mechanical Engineers ASME. 2017. OMAE2017-62253 https://doi.org/10.1115/OMAE2017-62253