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A saddlepoint approach to estimating joint extreme value distributions for vector non-stationary Gaussian processes
Vighnesh Ambetkar,
Published in Elsevier Ltd
2016
Volume: 58
   
Pages: 178 - 188
Abstract
The focus of this study is on estimating the multivariate extreme value distributions associated with a vector of mutually correlated non-stationary Gaussian processes. This involves computing the joint crossing statistics of the vector processes by assuming the crossings to be Poisson counting processes. A mathematical artifice is adopted to take into account the dependencies that exist between the crossings of the processes. The crux in the formulation lies in the evaluation of a high-dimensional integral, which can be computationally expensive. This difficulty is bypassed by using saddlepoint approximation. The developments are illustrated through two numerical examples and are validated using Monte Carlo simulations. In the second example, reliability analysis is carried out for a multiply supported pipeline on an offshore jacket structure subjected to wave loading using the proposed formulation. © 2016 Elsevier Ltd.
About the journal
JournalData powered by TypesetApplied Ocean Research
PublisherData powered by TypesetElsevier Ltd
ISSN01411187
Open AccessNo
Concepts (24)
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    Estimation
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    Gaussian noise (electronic)
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    Intelligent systems
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    Monte carlo methods
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    Poisson distribution
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    Probability distributions
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    Reliability analysis
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    Counting process
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    Extreme value distributions
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    HIGH-DIMENSIONAL INTEGRALS
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    MULTI-VARIATE EXTREME-VALUE DISTRIBUTION
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    Nonstationary
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    OFFSHORE JACKET STRUCTURES
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    SADDLE-POINT APPROXIMATION
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    WAVE LOADINGS
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    Gaussian distribution
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    Computer simulation
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    Estimation method
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    Gaussian method
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    Monte carlo analysis
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    Multivariate analysis
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    Offshore structure
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    Pipeline
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    Poisson ratio