The DOI matches its Crossref record.
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
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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)
Citations
Cites
Still being checked.
Cited by
Sorted by citations
Items 901–1,000 of 1,000
Modeling dependency between industry production and energy market via stochastic copula approach
Yıldırım, E., & Cengiz, M. A. (2019) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610918.2019.1691228 · Cited by 3
Economic losses in carbon forestry due to errors in inventory data
Ruotsalainen, R., Pukkala, T., Kangas, A. S., Myllymäki, M., & Packalén, P. (2020) · Canadian Journal of Forest Research · doi:10.1139/cjfr-2020-0251 · Cited by 3
Multivariate medial correlation with applications
Ferreira, H., & Феррейра, М. (2020) · Dependence Modeling · doi:10.1515/demo-2020-0019 · Cited by 3
Empirical comparison of skewed t-copula models for insurance and financial data
Huang, L., & Shemyakin, A. E. (2020) · Model Assisted Statistics and Applications · doi:10.3233/mas-200506 · Cited by 3
mvClaim : an R package for multivariate general insurance claims severity modelling
Hu, S. Z., Murphy, T. B., & O’Hagan, A. R. (2021) · Annals of Actuarial Science · doi:10.1017/s1748499521000099 · Cited by 3
Dynamics of HDI Index: Temporal Dependence Based on D-vine Copulas Model for Three-Way Data
Ruscone, M. N., & Fernández, D. (2021) · Social Indicators Research · doi:10.1007/s11205-021-02682-y · Cited by 3
Sequential Truncation of R‐Vine Copula Mixture Model for High‐Dimensional Datasets
Alanazi, F. A. (2021) · International Journal of Mathematics and Mathematical Sciences · doi:10.1155/2021/3214262 · Cited by 3
Nonparametric estimator of the tail dependence coefficient: balancing bias and variance
Garcin, M., & Nicolas, M. L. D. (2024) · Statistical Papers · doi:10.1007/s00362-024-01582-w · Cited by 3
Bayesian geostatistical modeling for discrete‐valued processes
Zheng, X., Kottas, A., & Sansó, B. (2023) · Environmetrics · doi:10.1002/env.2805 · Cited by 3
Interactive Visualization and Computation of 2D and 3D Probability Distributions
Bobrovnikov, M., Chai, J. T., & Dinov, I. D. (2022) · SN Computer Science · doi:10.1007/s42979-022-01206-w · Cited by 3
Residual‐based cumulative sum charts to monitor time series of counts via copula‐based Markov models
Alqawba, M., Kim, J., & Radwan, T. (2022) · Applied Stochastic Models in Business and Industry · doi:10.1002/asmb.2703 · Cited by 3
Is age at menopause decreasing? – The consequences of not completing the generational cohort
Martins, R., de Sousa, B. C., Kneib, T., Hohberg, M., Klein, N., Duarte, E., & Rodrigues, V. (2022) · BMC Medical Research Methodology · doi:10.1186/s12874-022-01658-x · Cited by 3
Ma, J., Li, X., Liu, J., Cui, J., Zhang, M., & Sriboonchitta, S. (2022) · Axioms · doi:10.3390/axioms11120669 · Cited by 3
Dependence Modelling of Frequency-Severity of Insurance Claims Using Waiting Time for Claim
Gao, G., & Li, J. (2021) · Insurance Mathematics and Economics · doi:10.1016/j.insmatheco.2022.12.006 · Cited by 3
Theoretical Advancements on a Few New Dependence Models Based on Copulas with an Original Ratio Form
Chesneau, C. (2023) · Modelling—International Open Access Journal of Modelling in Engineering Science · doi:10.3390/modelling4020008 · Cited by 3
Parameterized transformations and truncation: When is the result a copula?
Saminger‐Platz, S., Kolesárová, A., Šeliga, A., Mesiar, R., & Klement, E. P. (2023) · Journal of Computational and Applied Mathematics · doi:10.1016/j.cam.2023.115340 · Cited by 3
Elliptical and Skew-Elliptical Regression Models and Their Applications to Financial Data Analytics
Dewick, P. R., Liu, S., Liu, Y., & Ma, T. (2023) · Journal of risk and financial management · doi:10.3390/jrfm16070310 · Cited by 3
Addressing regressors’ endogeneity and the identification of true parameters
Christopoulos, D., Smyrnakis, D., & Tzavalis, E. (2025) · Econometrics and Statistics · doi:10.1016/j.ecosta.2025.04.002 · Cited by 3
A directory of families of infinitely extendible Archimedean copulas
Kimberling, C. H. (2015) · Fuzzy Sets and Systems · doi:10.1016/j.fss.2015.10.004 · Cited by 3
On multivariate countermonotonic copulas and their actuarial application
Ko, B., & Ahn, J. Y. (2016) · Lobachevskii Journal of Mathematics · doi:10.1134/s199508021604003x · Cited by 3
An asymptotic characterization of hidden tail credit risk with actuarial applications
Yuan, Z. (2017) · European Actuarial Journal · doi:10.1007/s13385-017-0150-6 · Cited by 3
Peña, V., & Ibragimov, R. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-809818-9.00006-9 · Cited by 3
Aggregation functions in fuzzy set theory: History and some recent advances
Mesiar, R., & Kolesárová, A. (2018) · Iranian Joint Congress on Fuzzy and Intelligent Systems · doi:10.1109/cfis.2018.8336641 · Cited by 3
Wang, W., Song, L., Chen, P., & Ding, S. (2019) · Journal of Educational Measurement · doi:10.1111/jedm.12200 · Cited by 3
Model and Moment Selection in Factor Copula Models
Duan, F., Manner, H., & Wied, D. (2019) · Journal of Financial Econometrics · doi:10.1093/jjfinec/nbz039 · Cited by 3
The dynamics and volatility of prices in multiple markets: a quantile approach
Chavas, J. (2020) · Empirical Economics · doi:10.1007/s00181-020-01821-7 · Cited by 3
AVALIAÇÃO DA QUALIDADE DA ÁGUA NA AMAZÔNIA BRASILEIRA: ESTUDO DE CASO NA BACIA DO RIO PEIXE-BOI-PA
dos Santos, L. P. C., Pereira, S. F., S., P. M., & Silva, C. S. (2020) · Revista SODEBRAS · doi:10.29367/issn.1809-3957.15.2020.180.69 · Cited by 3
Tsoukalas, I. (2021) · Hydrological Sciences Journal · doi:10.1080/02626667.2021.2014056 · Cited by 3
High-Dimensional Radial Symmetry of Copula Functions: Multiplier Bootstrap vs. Randomization
Billio, M., Frattarolo, L., & Guégan, D. (2022) · Symmetry · doi:10.3390/sym14010097 · Cited by 3
Improving modeling of stochastic processes by smart denoising
Jelenčič, J., & Mladenić, D. (2022) · Informatica · doi:10.31449/inf.v46i1.3875 · Cited by 3
Modelling the Dependence Structure of Financial Assets: A Bivariate Extreme Data Study
Budiarti, R., Wigena, A. H., Purnaba, I. G. P., & Achsani, N. A. (2018) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/187/1/012003 · Cited by 3
On the structure of exchangeable extreme-value copulas
Mai, J.-F., & Scherer, M. A. (2020) · Journal of Multivariate Analysis · doi:10.1016/j.jmva.2020.104670 · Cited by 3
Tail Risk Protection in Asset ManagementPreprint
Homescu, C. (2014) · SSRN Electronic Journal · doi:10.2139/ssrn.2524483 · Cited by 3
Predicting MMSE Score from Finger-Tapping MeasurementPreprint
Ma, J. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/817338 · Cited by 3
Copula Averaging for Tail Dependence in Insurance Claims DataPreprint
Hu, S. Z., & O’Hagan, A. R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.10912 · Cited by 3
Astivia, O. L. O. (2019) · doi:10.31234/osf.io/frmnx · Cited by 3
Copula-Based Normalizing FlowsPreprint
Laszkiewicz, M., Lederer, J., & Fischer, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.07352 · Cited by 3
The Creation of Puffin, the Automatic Uncertainty CompilerPreprint
Gray, N., de Angelis, M., & Ferson, S. A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.10153 · Cited by 3
A Synthetic European Weather Dataset Based on Spatiotemporal Vine Copulas
Claassen, J., Koks, E., de Ruiter, M., Ward, P. J., & Jäger, W. (2025) · Scientific Data · doi:10.1038/s41597-025-06015-3 · Cited by 3
Kuschinski, N., & Jara, A. (2023) · Bayesian Analysis · doi:10.1214/23-ba1396 · Cited by 2
A unified Gaussian copula methodology for spatial regression analysis
Hughes, J. R. (2022) · Scientific Reports · doi:10.1038/s41598-022-20171-1 · Cited by 2
Sy, M. M., Conrad, A., Jung, C., Lindtner, O., & Greiner, M. (2023) · Exposure and Health · doi:10.1007/s12403-023-00573-w · Cited by 2
Forecasting Natural Gas Prices with Spatio-Temporal Copula-Based Time Series Models
Pappert, S., & Arsova, A. (2023) · Contributions to statistics · doi:10.1007/978-3-031-40209-8_15 · Cited by 2
Górecki, J., & Okhrin, O. (2024) · SpringerBriefs in applied statistics and econometrics · doi:10.1007/978-3-031-56337-9_1 · Cited by 2
Gräfensteiner, P., Löwer, E., Furat, O., Peuker, U. A., & Schmidt, V. (2024) · Powder Technology · doi:10.1016/j.powtec.2024.120550 · Cited by 2
An unsupervised feature extraction and fusion framework for multi-source data based on copula theory
Chen, X., Lai, L., & Luo, M. (2025) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2025.109384 · Cited by 2
Landowner Willingness to Adopt Active Management Practices in the Cross-Timbers Forests in USA
Rodrigo, C., Joshi, O., Poudyal, N. C., Russell, A., & Susaeta, A. I. (2025) · Forest Policy and Economics · doi:10.1016/j.forpol.2025.103522 · Cited by 2
On the pointwise supremum of the set of copulas with a given curvilinear section
Ouyang, Y., Sun, Y., & Zhang, H.-P. (2025) · Fuzzy Sets and Systems · doi:10.1016/j.fss.2025.109515 · Cited by 2
Copula-based Modeling of Sea Surface Temperature and Chlorophyll-a Distributions
Salah, A. B., El‐Amrani, M., Zellou, B., Seaı̈d, M., & Moçayd, N. E. (2025) · Mathematical Geosciences · doi:10.1007/s11004-025-10201-9 · Cited by 2
Biancalana, D., Baione, F., & Marchione, M. M. (2025) · Annals of Operations Research · doi:10.1007/s10479-025-06714-5 · Cited by 2
Dependence modelling across major causes of death via time-varying copula state space models
Hanebeck, A., Li, H., & Czado, C. (2025) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1093/jrsssc/qlaf049 · Cited by 2
Lerat, J., Vaze, J., & Wang, Q. J. (2026) · Journal of Hydrology · doi:10.1016/j.jhydrol.2026.135877 · Cited by 2
Yang, Y., Feng, H., Yin, Z., Zhao, W., Yan, Z., & Zhang, Y. (2025) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2025.146842 · Cited by 2
Vázquez-Ramírez, D., Díaz-Viera, M. A., & del Valle‐García, R. (2025) · Petroleum Science · doi:10.1016/j.petsci.2025.10.029 · Cited by 2
Tenorio‐Salgueiro, S., Salgueiro, A. M., Lado‐Sestayo, R., & Búa, M. V. (2025) · Business Strategy and the Environment · doi:10.1002/bse.70309 · Cited by 2
A vine copula-based analysis of spatial dependence of traffic conflict risk at highway ramp areas
Gu, R., & Sze, N. N. (2025) · Accident Analysis & Prevention · doi:10.1016/j.aap.2025.108330 · Cited by 2
Air and Water Temperature Relationships in Major Polish Rivers and Their Long-Term Changes
Ptak, M., Sojka, M., Szyga–Pluta, K., & Amnuaylojaroen, T. (2025) · Sustainability · doi:10.3390/su172310737 · Cited by 2
Integer-Valued Time Series Model via Copula-Based Bivariate Skellam Distribution
Alqawba, M., Diawara, N., & Sene, M. M. (2026) · Journal of risk and financial management · doi:10.3390/jrfm19010027 · Cited by 2
Statistical Analysis and Modeling of the 3D Morphology and Texture of Lunar Regolith Simulants
Weber, M., Ditscherlein, R., Ditscherlein, L., Birch, T., Fränz, M., Seidel, A., Peuker, U. A., Furat, O., Schmidt, V., & Pöhle, G. (2026) · Microscopy and Microanalysis · doi:10.1093/mam/ozag013 · Cited by 2
Georgescu, I. A., & Kinnunen, J. (2026) · Entropy · doi:10.3390/e28030344 · Cited by 2
Bivariate postprocessing of wind vectors
Buchner, F., Jobst, D., Möller, A., & Czado, C. (2026) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.70188 · Cited by 2
Mathematical Analysis and Computational Approximation of Extremal Points of the Subset of Copulas
Jaafar, R., Hfa, A., & Sani, A. (2026) · Stats · doi:10.3390/stats9030066 · Cited by 2
Dynamic Dependence and Tail Risk in Technology, Cryptocurrency and Commodity Markets
Georgescu, I. A. (2026) · Applied Sciences · doi:10.3390/app16136537 · Cited by 2
Copula-based models for competing risks: a comprehensive review
Majakwara, J., & Hove, H. (2026) · Statistical Papers · doi:10.1007/s00362-026-01871-6 · Cited by 2
Mohammadi, M., Emadi, M., & Amini, M. (2022) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610918.2022.2025836 · Cited by 2
Ghahramani, M., White, S. S., & de Leon, A. R. (2022) · Journal of Statistics and Management Systems · doi:10.1080/09720510.2021.1960550 · Cited by 2
Zhang, C., & Czado, C. (2023) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1093/jrsssc/qlad038 · Cited by 2
On the Diversification Effect in Solvency II for Extremely Dependent Risks
Chen, Y., Cheung, K. C., Yam, S. C. P., Yuen, F. L., & Zeng, J. (2023) · Risks · doi:10.3390/risks11080143 · Cited by 2
Ayala, A. L., Blazsek, S., & Licht, A. (2023) · Studies in Nonlinear Dynamics and Econometrics · doi:10.1515/snde-2022-0107 · Cited by 2
Modeling asymmetrically dependent automobile bodily injury claim data using Khoudraji Copulas
Kara, E. K. (2023) · Sigma Journal of Engineering and Natural Sciences – Sigma Mühendislik ve Fen Bilimleri Dergisi · doi:10.14744/sigma.2023.00133 · Cited by 2
A copula formulation for multivariate latent Markov models
Russo, A., & Farcomeni, A. (2024) · Test · doi:10.1007/s11749-024-00919-9 · Cited by 2
Liquidity‐adjusted value‐at‐risk using extreme value theory and copula approach
Kamal, H., & Paul, S. (2024) · Journal of Forecasting · doi:10.1002/for.3105 · Cited by 2
Gabriel, B. J., Moreno, L., & Scaglione, M. (2024) · Quality & Quantity · doi:10.1007/s11135-024-01839-4 · Cited by 2
Joint probabilistic behaviour of climate extremes over the Godavari River basin, India
Kumar, S., Das, P., Mandal, N., Chanda, K., & Pasupuleti, S. (2024) · International Journal of Climatology · doi:10.1002/joc.8486 · Cited by 2
Kurowicka, D., Aspinall, W. P., & Cooke, R. M. (2024) · Entropy · doi:10.3390/e26110949 · Cited by 2
Lagrangian Identification of Bulgaria’s Moisture Sources: A Key to Understanding Drought Dynamics
Sorí, R., Stojanovic, M., Guerova, G., Pérez‐Alarcón, A., Vázquez, M., Ernst, J., Nieto, R., & Gimeno, L. (2024) · Earth Systems and Environment · doi:10.1007/s41748-024-00542-6 · Cited by 2
Multivariate Risk Analysis in Cryptocurrency Market: An Optimal Transport Approach
Franco, J. P. M., & Laurini, M. P. (2025) · Computational Economics · doi:10.1007/s10614-025-10888-2 · Cited by 2
Bayesian sequential stock return prediction through copulas
Virbickaitė, A., Frey, C., & Macedo, D. N. (2020) · The Journal of Economic Asymmetries · doi:10.1016/j.jeca.2020.e00173 · Cited by 2
CD-vine model for capturing complex dependence
Evkaya, O., Yozgatlıgil, C., & Kestel, S. (2020) · Journal of Applied Statistics · doi:10.1080/02664763.2020.1834519 · Cited by 2
A multivariate Poisson model based on comonotonic shocks
Schulz, J., Genest, C., & Mesfioui, M. (2020) · International Statistical Review · doi:10.1111/insr.12408 · Cited by 2
Factor copula models for right-censored clustered survival data
Campos, E., Braekers, R., de Souza, D. J., & Chaves, L. M. (2021) · Lifetime Data Analysis · doi:10.1007/s10985-021-09525-5 · Cited by 2
Coordinate-wise transformation of probability distributions to achieve a Stein-type identity
Sei, T. (2021) · Information Geometry · doi:10.1007/s41884-021-00051-9 · Cited by 2
Мозговий, А. О., & Spirande, K. (2021) · E3S Web of Conferences · doi:10.1051/e3sconf/202128003007 · Cited by 2
Smooth bootstrapping of copula functionals
Coblenz, M., Grothe, O., Herrmann, K., & Hofert, M. (2022) · Electronic Journal of Statistics · doi:10.1214/22-ejs2007 · Cited by 2
Theoretical considerations when simulating data from the g ‐and‐ h family of distributions
Astivia, O. L. O., & Kroc, E. (2022) · British Journal of Mathematical and Statistical Psychology · doi:10.1111/bmsp.12274 · Cited by 2
Theoretical Validation of New Two-Dimensional One-Variable-Power Copulas
Chesneau, C. (2023) · Axioms · doi:10.3390/axioms12040392 · Cited by 2
UNCERTAINTYQUANTIFICATION.JL: A NEW FRAMEWORK FOR UNCERTAINTY QUANTIFICATION IN JULIA
Behrensdorf, J., Gray, A., Broggi, M., & Beer, M. (2023) · UNCECOMP Proceedings · doi:10.7712/120223.10347.19810 · Cited by 2
Huang, Z., Kwok, Y. K., & Xu, Z. (2024) · Insurance Mathematics and Economics · doi:10.1016/j.insmatheco.2024.01.005 · Cited by 2
Smooth test for equality of copulas
Bakam, Y. I. N., & Pommeret, D. (2024) · Electronic Journal of Statistics · doi:10.1214/24-ejs2220 · Cited by 2
Nonparametric Estimation of Conditional Copula Using Smoothed Checkerboard Bernstein Sieves
Lu, L., & Ghosh, S. K. (2024) · Mathematics · doi:10.3390/math12081135 · Cited by 2
Changes in the Dependence Structure of AROPE Components: Evidence from the Spanish Regions
de Valladolid, U., García‐Gómez, C., Pérez, A., de Valladolid, U., Alaiz, M. P., & de Valladolid, U. (2024) · Revista Hacienda Pública Española · doi:10.7866/hpe-rpe.24.1.2 · Cited by 2
Construction of copulas for bivariate failure rates
Wu, S., Dui, H., & Hu, L. (2024) · Annals of Operations Research · doi:10.1007/s10479-024-06053-x · Cited by 2
Invariant correlation under marginal transforms
Koike, T., Lin, L., & Wang, R. (2024) · Journal of Multivariate Analysis · doi:10.1016/j.jmva.2024.105361 · Cited by 2
Cutting Feedback in Misspecified Copula Models
Smith, M. S., Yu, W., Nott, D. J., & Frazier, D. T. (2025) · Journal of the American Statistical Association · doi:10.1080/01621459.2025.2464270 · Cited by 2
On Italian Families’ Inability to Meet Expected Expenses: A Possible Measure and Its Determinants
Condino, F., & Domma, F. (2025) · Italian Economic Journal · doi:10.1007/s40797-025-00331-8 · Cited by 2
Khan, I., & Rahman, S. U. (2025) · Computational Economics · doi:10.1007/s10614-025-11040-w · Cited by 2
Using Auxiliary Marginal Quantiles for Gaussian Copula Models with Nonignorable Missing Data
Feldman, J., Reiter, J. P., & Kowal, D. R. (2025) · Bayesian Analysis · doi:10.1214/25-ba1551 · Cited by 2
Policy Learning with Distributional Welfare
Cui, Y., & Han, S. (2025) · Journal of the American Statistical Association · doi:10.1080/01621459.2025.2552514 · Cited by 2
Kim, J., Ha, I. D., & Kim, S.-J. (2025) · Computational Statistics · doi:10.1007/s00180-025-01669-4 · Cited by 2
Aggregation Functions as a Base for Decision Making
Pap, E. (2015) · International Scientific Conference Sinteza · doi:10.15308/synthesis-2015-143-146 · Cited by 2
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review