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Dependence Modeling with Copulas
Abstract
Dependence Modeling with Copulas covers the substantial advances that have taken place in the field during the last 15 years, including vine copula modeling of high-dimensional data. Vine copula models are constructed from a sequence of bivariate copulas. The book develops generalizations of vine copula models, including common and structured facto
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“As described in Joe (2014), Section 5.5, much of theory needed to justify composite likelihood methods in a copula setting can be provided through estimating equations.”
From Dependence modeling of multivariate longitudinal hybrid insurance data with dropout (2021) · cites this paper as Joe (2014)
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Compounding effects of sea level rise and fluvial flooding
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Measuring compound flood potential from river discharge and storm surge extremes at the global scale
Couasnon, A., Eilander, D., Muis, S., Veldkamp, T., Haigh, I. D., Wahl, T., Winsemius, H., & Ward, P. J. (2020) · Natural hazards and earth system sciences · doi:10.5194/nhess-20-489-2020 · Cited by 343
Metadata
- Authors
- Joe, H.
- Corresponding author
- Joe, Harry
- ORCID
- Joe, Harry
- Venue
- Chapman & Hall/CRC eBooks
- Publisher
- Chapman and Hall/CRC
- Published
- 2014-06-26
- OpenAlex
- W390686421
- Cited by
- 1,566
- References
- 0
- Citations by year
- 2026: 147 · 2025: 142 · 2024: 169 · 2023: 154 · 2022: 153 · 2021: 153 · 2020: 169 · 2019: 142 · 2018: 108 · 2017: 108 · 2016: 67 · 2015: 45 · 2014: 7
- FWCI
- 78.39
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Chapman & Hall/CRC eBooks (publishedVersion)
- International Linear Collider (submittedVersion)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Finance › Financial Risk and Volatility Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Social Sciences › Decision Sciences › Management Science and Operations Research › Probability and Risk Models
- Keywords
- copulas, dependence modeling, high-dimensional data, bivariate copulas, common factor model, multivariate dependence
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