Calculation of Joint Return Period for Connected Edge Data

For better displaying the statistical properties of measured data, it is particularly important to select a suitable multivariate joint distribution model in ocean engineering. According to the characteristics and properties of Copula functions and the correlation analysis of measured data, the nonl...

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Bibliographic Details
Published inWater (Basel) Vol. 11; no. 2; p. 300
Main Authors Liu, Guilin, Chen, Baiyu, Gao, Zhikang, Fu, Hanliang, Jiang, Song, Wang, Liping, Yi, Kou
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 11.02.2019
Subjects
engineering
Ocean engineering
Ocean waves
Parameter estimation
Random variables
Wind
wind speed
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Summary:For better displaying the statistical properties of measured data, it is particularly important to select a suitable multivariate joint distribution model in ocean engineering. According to the characteristics and properties of Copula functions and the correlation analysis of measured data, the nonlinear relationship between random variables can be captured. Additionally, the models based on the Copula theory have more general applicability. A series of correlation measure index, derived from Copula functions, can expand the correlation measure range among variables. In this paper, by means of the correlation analysis between the annual extreme wave height and the corresponding wind speed, their joint distribution models were studied. The newly established two-dimensional joint distribution functions of the extreme wave height and the corresponding wind speed were compared with the existing two-dimensional joint distributions.
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ISSN:2073-4441
2073-4441
DOI:10.3390/w11020300