Copula–entropy theory for multivariate stochastic modeling in water engineering

Singh, Vijay P.; Zhang, Lan

doi:10.1186/s40562-018-0105-z

Cited by 30 publications

(8 citation statements)

References 29 publications

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“…Since the Gumbel-Hougaard copula can detect positive dependence, it is used in modeling bivariate flood frequency, storm, drought, etc. (see, [14]). [15] claimed that no closed-form expression exists for Spearman's rho.…”

Section: Example 1 (Fgm Copula)mentioning

confidence: 98%

Construction of a bivariate copula by Rüschendorf’s method

Yılmaz

Bekçi

2021

Cumhuriyet Science Journal

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In this paper, a new copula model with given unit marginals is proposed, based on Rüschendorf's Method. A new bivariate copula family is introduced by adding a proper term to independence copula. Thus, we avoid the complexity of the proposed copula model. By choosing a baseline copula from the same marginal, we derive a new copula that can approach from above towards the independence copula. Furthermore, it is established that a bivariate copula constructed by this method allows some flexibility in the dependence measure according to Spearman's correlation coefficient. Additionally, tail dependence measures are investigated. Illustrative examples are given taking into account the specific choices of a baseline copula.

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Section: Example 1 (Fgm Copula)mentioning

confidence: 98%

Construction of a bivariate copula by Rüschendorf’s method

Yılmaz

Bekçi

2021

Cumhuriyet Science Journal

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“…The return period refers to the time when the value of the random variable appears in a longer period (Singh and Zhang, 2018).…”

Section: The Return Period Of Normalized Difference Vegetation Index mentioning

confidence: 99%

Influence of Variations of Hydrothermal Conditions on Normalized Difference Vegetation Index in Typical Temperature Zones Along the East Coast of China

He¹,

Zhen

et al. 2020

Front. Earth Sci.

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“…The return period refers to the time when the value of the random variable appears in a longer period [69]. Calculating the return periods of the NDVI under different maximum and minimum temperature conditions can provide valuable information for a more meticulous study of daytime and nighttime warming asymmetry affect the NDVI.…”

Section: The Return Period Of Ndvi and Tmax/tminmentioning

confidence: 99%

Asymmetry of Daytime and Nighttime Warming in Typical Climatic Zones along the Eastern Coast of China and Its Influence on Vegetation Activities

2020

Remote Sensing

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In this dissertation, the author adopted the normalized difference vegetation index (NDVI) and meteorological data from 1982 to 2016 of the typical climate zones in coastal areas of China to analyze the influence of daytime and nighttime warming asymmetric changes in different seasons on vegetation activities during the growing season period according to the copula function theory optimized based on Markov chain Monte Carlo (MCMC). The main conclusions are as follows: (1) The seasonal daytime and nighttime warming trends of Guangdong, Jiangsu and Liaoning over the past 35 years were significant, and the daytime and nighttime warming rates were asymmetric. In spring and summer of Guangdong province, the warming rate in the daytime was higher than that at night, while, in autumn, the opposite law was observed. However, the warming rate in the daytime was lower than that at night in Jiangsu and Liaoning provinces. There were latitude differences in diurnal and nocturnal warming rate. (2) The daytime and nighttime warming influences on vegetation showed significant seasonal differences in these three regions. In Guangdong, the influence of nighttime warming on vegetation growth in spring is greater than that in summer, and the influences of daytime warming on vegetation growth from strong to weak were spring, summer and autumn. In Jiangsu, both the influences of daytime and nighttime warming on vegetation growth in summer were less than that in autumn. In Liaoning, both the influences of daytime and nighttime warming on vegetation growth from strong to weak were autumn, spring and summer. (3) In Guangdong, Jiangsu and Liaoning provinces, their maximum temperature (Tmax) and minimum temperature (Tmin) and the joint probability distribution functions of NDVI, all had little effect on NDVI when Tmax and Tmin respectively reached their minimum values, but their influences on NDVI were obvious when Tmax and Tmin respectively reached their maximum values. (4) The smaller the return period, the larger the range of climate factor and NDVI, which has indicated that when the climate factor is certain, the NDVI is more likely to have a smaller return period, and the frequency of NDVI over a certain period is higher. In addition, the larger the climate factor, the greater the return period is and NDVI is less frequent over a certain period of time. This research can help with deep understanding of the dynamic influence of seasonal daytime and nighttime asymmetric warming on the vegetation in typical coastal temperature zones of China under the background of global climate change.

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Copula–entropy theory for multivariate stochastic modeling in water engineering

Cited by 30 publications

References 29 publications

Construction of a bivariate copula by Rüschendorf’s method

Construction of a bivariate copula by Rüschendorf’s method

Influence of Variations of Hydrothermal Conditions on Normalized Difference Vegetation Index in Typical Temperature Zones Along the East Coast of China

Asymmetry of Daytime and Nighttime Warming in Typical Climatic Zones along the Eastern Coast of China and Its Influence on Vegetation Activities

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