Dependence Modeling with Copulas

Statistical inference for streamed longitudinal data

Luo, L., Wang, J., & Hector, E. C. (2023) · Biometrika · doi:10.1093/biomet/asad010 · Cited by 17

Storm surge hazard estimation along the US Gulf Coast: A Bayesian hierarchical approach

Boumis, G., Moftakhari, H. R., & Moradkhani, H. (2023) · Coastal Engineering · doi:10.1016/j.coastaleng.2023.104371 · Cited by 17

Copula modeling from Abe Sklar to the present day

Genest, C., Okhrin, O., & Bodnar, T. (2023) · Journal of Multivariate Analysis · doi:10.1016/j.jmva.2023.105278 · Cited by 17

Performance Analysis of SWIPT Relay Networks Over Arbitrary Dependent Fading Channels

Ghadi, F. R., & López‐Martínez, F. J. (2024) · IEEE Transactions on Communications · doi:10.1109/tcomm.2024.3359034 · Cited by 17

Projected changes in monsoonal compound dry-hot extremes in India

Kumar, N., & Goyal, M. K. (2024) · Atmospheric Research · doi:10.1016/j.atmosres.2024.107605 · Cited by 17

Tail-weighted dependence measures with limit being the tail dependence coefficient

Lee, D., Joe, H., & Krupskii, P. (2017) · Journal of nonparametric statistics · doi:10.1080/10485252.2017.1407414 · Cited by 17

A non-stationary cost-benefit based bivariate extreme flood estimation approach

Qi, W., & Liu, J. (2017) · Journal of Hydrology · doi:10.1016/j.jhydrol.2017.12.045 · Cited by 17

Generalized Pareto copulas: A key to multivariate extremes

Falk, M., Padoan, S. A., & Wisheckel, F. (2019) · Journal of Multivariate Analysis · doi:10.1016/j.jmva.2019.104538 · Cited by 17

Heterogeneous tail generalized COMFORT modeling via Cholesky decomposition

Näf, J., Paolella, M. S., & Polak, P. (2019) · Journal of Multivariate Analysis · doi:10.1016/j.jmva.2019.02.004 · Cited by 17

Structured expert judgement to understand the intrinsic vulnerability of traffic networks

Nogal, M., Morales‐Nápoles, O., & O’Connor, A. (2019) · Transportation Research Part A Policy and Practice · doi:10.1016/j.tra.2019.07.006 · Cited by 17

Measurement of Systemic Risk in Global Financial Markets and Its Application in Forecasting Trading Decisions

Liu, J., Song, Q., Yang, Q., Rahman, S., & Sriboonchitta, S. (2020) · Sustainability · doi:10.3390/su12104000 · Cited by 17

Probabilistic long-term reservoir operation employing copulas and implicit stochastic optimization

Ávila, L., Mine, M., & Kaviski, E. (2020) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-020-01826-9 · Cited by 17

CopulaCenR: Copula based Regression Models for Bivariate Censored Data in R

Sun, T., & Ding, Y. (2020) · The R Journal · doi:10.32614/rj-2020-025 · Cited by 17

Stability of spatial dependence structure of extreme precipitation and the concurrent risk over a nested basin

Liu, Z., Xie, Y., Cheng, L., Lin, K., Tu, X., & Chen, X. (2021) · Journal of Hydrology · doi:10.1016/j.jhydrol.2021.126766 · Cited by 17

The effect of obesity on chronic diseases in USA: a flexible copula approach

Dettoni, R., Bahamondes, C., Yevenes, C. F., Céspedes, C., & Espinosa, J. (2023) · Scientific Reports · doi:10.1038/s41598-023-28920-6 · Cited by 17

Credit and Systemic Risks in the Financial Services Sector: Evidence From the 2008 Global Crisis

Bégin, J., Boudreault, M., Doljanu, D.-A., & Gauthier, G. (2017) · Journal of Risk & Insurance · doi:10.1111/jori.12210 · Cited by 17

Analyzing within garage fuel economy gaps to support vehicle purchasing decisions – A copula-based modeling & forecasting approach

Wali, B., Greene, D. L., Khattak, A. J., & Liu, J. (2018) · Transportation Research Part D Transport and Environment · doi:10.1016/j.trd.2018.04.023 · Cited by 17

Characterization of Precipitation through Copulas and Expert Judgement for Risk Assessment of Infrastructure

Morales‐Nápoles, O., Paprotny, D., Worm, D. T. H., Abspoel, L., & Courage, W. M. G. (2017) · ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part A Civil Engineering · doi:10.1061/ajrua6.0000914 · Cited by 17

Enhancing health risk assessment for soil heavy metal (loid)s using a copula-based monte carlo simulation method

Wang, X., Xu, F., Gui, Y., & Liu, Y. (2025) · Ecotoxicology and Environmental Safety · doi:10.1016/j.ecoenv.2025.118419 · Cited by 16

Copula-Based Vulnerability Analysis of Civil Infrastructure Subjected to Hurricanes

Li, Y., Dong, Y., & Zhu, D. (2020) · Frontiers in Built Environment · doi:10.3389/fbuil.2020.571911 · Cited by 16

Influence of nonstationarity and dependence of extreme wave parameters on the reliability assessment of coastal structures - A case study

Radfar, S., & Γαλιατσάτου, Π. (2023) · Ocean Engineering · doi:10.1016/j.oceaneng.2023.113862 · Cited by 16

Is a Normal Copula the Right Copula?

Amengual, D., & Sentana, E. (2018) · Journal of Business and Economic Statistics · doi:10.1080/07350015.2018.1505631 · Cited by 16

An invitation to coupling and copulas: With applications to multisensory modeling

Colonius, H. (2016) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2016.02.004 · Cited by 16

Tests of radial symmetry for multivariate copulas based on the copula characteristic function

Bahraoui, T., & Quessy, J. (2017) · Electronic Journal of Statistics · doi:10.1214/17-ejs1280 · Cited by 16

Variational inference for high dimensional structured factor copulas

Nguyen, H., Ausín, M. C., & Galeano, P. (2020) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2020.107012 · Cited by 16

Fusion of Deep Neural Networks for Activity Recognition: A Regular Vine Copula Based Approach

Zhang, S., Geng, B., Varshney, P. K., & Rangaswamy, M. (2019) · International Conference on Information Fusion (FUSION) · doi:10.23919/fusion43075.2019.9011282 · Cited by 16

Effects of seasonal and interannual variability in water isotopes (δ2H, δ18O) on estimates of water balance in a chain of seven prairie lakes

Haig, H. A., Hayes, N. M., Simpson, G. L., Yi, Y., Wissel, B., Hodder, K. R., & Leavitt, P. R. (2020) · Journal of Hydrology X · doi:10.1016/j.hydroa.2020.100069 · Cited by 16

Modeling Multivariate Time Series With Copula-Linked Univariate D-Vines

Zhao, Z., Shi, P., & Zhang, Z. (2020) · Journal of Business and Economic Statistics · doi:10.1080/07350015.2020.1859381 · Cited by 16

Risk concentration and the mean‐expected shortfall criterion

Han, X., Wang, B., Wang, R., & Wu, Q. (2023) · Mathematical Finance · doi:10.1111/mafi.12417 · Cited by 16

Modeling the Characteristics of Unhealthy Air Pollution Events: A Copula Approach

Masseran, N. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18168751 · Cited by 16

A comparative study to determine the optimal copula model for the wind speed and precipitation of typhoons

Um, M., Joo, K., Nam, W., & Heo, J. (2016) · International Journal of Climatology · doi:10.1002/joc.4834 · Cited by 16

Functional generalizations of Hoeffding’s covariance lemma and a formula for Kendall’s tau

Lo, A. (2016) · Statistics & Probability Letters · doi:10.1016/j.spl.2016.11.016 · Cited by 16

Optimal moment determination in POME-copula based hydrometeorological dependence modelling

Liu, D., Wang, D., Singh, V. P., Wang, Y., Wu, J., Wang, L., Zou, X., Chen, Y., & Chen, X. (2017) · Advances in Water Resources · doi:10.1016/j.advwatres.2017.04.016 · Cited by 16

Detection of block-exchangeable structure in large-scale correlation matrices

Perreault, S., Duchesne, T., & Nešlehová, J. (2018) · Journal of Multivariate Analysis · doi:10.1016/j.jmva.2018.10.009 · Cited by 16

Low Flow Regimes of the Tarim River Basin, China: Probabilistic Behavior, Causes and Implications

Sun, P., Zhang, Q., Yao, R., Singh, V., & Song, C. (2018) · Water · doi:10.3390/w10040470 · Cited by 16

Nonparametric estimation of multivariate tail probabilities and tail dependence coefficients

Krupskii, P., & Joe, H. (2019) · Journal of Multivariate Analysis · doi:10.1016/j.jmva.2019.02.013 · Cited by 16

Focused information criteria for copulas

Ko, V., Hjort, N. L., & Haff, I. H. (2019) · Scandinavian Journal of Statistics · doi:10.1111/sjos.12387 · Cited by 16

Periodic Copula Autoregressive Model Designed to Multivariate Streamflow Time Series Modelling

Pereira, G. A. D. A., & Veiga, Á. (2019) · Water Resources Management · doi:10.1007/s11269-019-02308-6 · Cited by 16

Relation between non-exchangeability and measures of concordance of copulas

Bukovšek, D. K., Košir, T., Mojškerc, B., & Omladič, M. (2020) · Journal of Mathematical Analysis and Applications · doi:10.1016/j.jmaa.2020.123951 · Cited by 16

Bernstein copula-based spatial cosimulation for petrophysical property prediction conditioned to elastic attributes

Le, V. H., Díaz-Viera, M. A., Vázquez-Ramírez, D., del Valle‐García, R., Erdely, A., & Graña, D. (2020) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2020.107382 · Cited by 16

On a bivariate copula with both upper and lower full-range tail dependence

Hua, L. (2017) · Insurance Mathematics and Economics · doi:10.1016/j.insmatheco.2017.01.003 · Cited by 16

Condition prediction and estimation of service life in the presence of data censoring and dependent competing risks

Donev, V., & Hoffmann, M. H. W. (2017) · International Journal of Pavement Engineering · doi:10.1080/10298436.2017.1293264 · Cited by 16

A hydrological drought risk assessment method based on a four-dimensional Copula function model integrating development and recovery speed characteristics

Zhang, X., Wu, Z., Wang, H., Yu, Z., & Chen, Y. (2025) · Ecological Indicators · doi:10.1016/j.ecolind.2025.113751 · Cited by 15

Copula Regression for Compound Distributions with Endogenous Covariates with Applications in Insurance Deductible Pricing

Shi, P., & Lee, G. Y. (2022) · Journal of the American Statistical Association · doi:10.1080/01621459.2022.2040519 · Cited by 15

A general framework for portfolio construction based on generative models of asset returns

Cheng, T., & Chen, K. (2023) · The Journal of Finance and Data Science · doi:10.1016/j.jfds.2023.100113 · Cited by 15

Time‐Varying Linkage of Possible Safe Haven Assets: A Cross‐Market and Cross‐asset Analysis

Nguyen, P. M., & Liu, W. (2016) · International Review of Finance · doi:10.1111/irfi.12089 · Cited by 15

Dependency clustering of mixed data with Gaussian mixture copulas

Rajan, V., & Bhattacharya, S. (2016) · International Joint Conference on Artificial Intelligence · Cited by 15

Semiparametric Dynamic Max-Copula Model for Multivariate Time Series

Zhao, Z., & Zhang, Z. (2017) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/rssb.12256 · Cited by 15

Stochastic Simulation of Test Collections

Urbano, J., & Nagler, T. (2018) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3209978.3210043 · Cited by 15

Double trigger agricultural insurance products with weather index and yield index

Xiao, Y., & Yao, J. (2018) · China Agricultural Economic Review · doi:10.1108/caer-01-2018-0021 · Cited by 15

Nonparametric estimation of multivariate quantiles

Coblenz, M., Dyckerhoff, R., & Grothe, O. (2018) · Environmetrics · doi:10.1002/env.2488 · Cited by 16

Vine copula regression for observational studies

Cooke, R. M., Joe, H., & Chang, B. (2019) · AStA Advances in Statistical Analysis · doi:10.1007/s10182-019-00353-5 · Cited by 15

Bayesian analysis of multivariate crash counts using copulas

Park, E. S., Oh, R., Ahn, J. Y., & Oh, M. (2020) · Accident Analysis & Prevention · doi:10.1016/j.aap.2019.105431 · Cited by 15

Modeling Joint Relationship and Design Scenarios Between Precipitation, Surface Temperature, and Atmospheric Precipitable Water Over Mainland China

Ayantobo, O. O., Wei, J., & Wang, G. (2021) · Earth and Space Science · doi:10.1029/2020ea001513 · Cited by 15

Multivariate-drought indices—case studies with observations and outputs of NCAR CCSM-4 ensemble models

Tatlı, H. (2021) · Theoretical and Applied Climatology · doi:10.1007/s00704-021-03736-4 · Cited by 15

A Statistical Analysis of the Occurrences of Critical Waves and Water Levels for the Management of the Operativity of the MoSE System in the Venice Lagoon

Tomasicchio, G. R., Salvadori, G., Lusito, L., Francone, A., Frega, F., Saponieri, A., Leone, E., & Bartolo, S. G. D. (2021) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-021-02133-7 · Cited by 15

A Bayesian Non-Linear State Space Copula Model for Air Pollution in Beijing

Kreuzer, A., Valle, L. D., & Czado, C. (2022) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1111/rssc.12548 · Cited by 15

Research on PM2.5 Concentration Prediction Based on the CE-AGA-LSTM Model

Wu, X., Zhang, C., Zhu, J., & Zhang, X. (2022) · Applied Sciences · doi:10.3390/app12147009 · Cited by 15

On the singular components of a copula

Durante, F., Sánchez, J. F., & Trutschnig, W. (2015) · Journal of Applied Probability · doi:10.1017/s0021900200113154 · Cited by 15

Estimation of risk contributions with MCMC

Koike, T., & Minami, M. (2019) · Quantitative Finance · doi:10.1080/14697688.2019.1588469 · Cited by 15

Competing Risks Copula Models for Unemployment Duration: An Application to a German Hartz Reform

Lo, S., Stephan, G., & Wilke, R. A. (2015) · Journal of Econometric Methods · doi:10.1515/jem-2015-0005 · Cited by 15

FEASIBILITY OF THE USE OF HOMOGENEOUS HYDROLOGIC REGIONS IN THE MODELING OF A BRAZILIAN BASIN

Mendieta, J. D. P., & Hidalgo, I. G. (2020) · Revista SODEBRAS · doi:10.29367/issn.1809-3957.15.2020.180.106 · Cited by 14

CONCEPTUAL MODEL OF INFLOW FORECASTING FOR MEASUREMENT STATIONS AT SAO PAULO

ROCHA, T., ANGELIS, A. F., Lopes, J. E. G., & Hidalgo, I. G. (2020) · Revista SODEBRAS · doi:10.29367/issn.1809-3957.15.2020.180.117 · Cited by 14

On New Three- and Two-Dimensional Ratio-Power Copulas

Chesneau, C. (2023) · Computational Journal of Mathematical and statistical Sciences · doi:10.21608/cjmss.2023.195134.1005 · Cited by 14

Correcting Regressor-Endogeneity Bias via Instrument-Free Joint Estimation Using Semiparametric Odds Ratio Models

Qian, Y., & Xie, H. W. (2023) · Journal of Marketing Research · doi:10.1177/00222437231195577 · Cited by 16

Pair programming with ChatGPT for sampling and estimation of copulas

Górecki, J. (2023) · Computational Statistics · doi:10.1007/s00180-023-01437-2 · Cited by 15

Economic resilience:Measurement and assessment across time and space

Chavas, J. (2024) · Research in Economics · doi:10.1016/j.rie.2024.100953 · Cited by 14

Hierarchical variable clustering based on the predictive strength between random vectors

Fuchs, S., & Wang, Y. (2024) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2024.109185 · Cited by 14

ESG rating and ambiguity: an informative and distorted signal-based approach

bongermino, G., & Romagnoli, S. (2025) · Decisions in Economics and Finance · doi:10.1007/s10203-025-00507-y · Cited by 14

Combining parametric low‐dimensional ensemble postprocessing with reordering methods

Schefzik, R. (2016) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.2839 · Cited by 14

Introduction to Extreme Value Theory: Applications to Risk Analysis and Management

Kratz, M. F. (2019) · MATRIX book series · doi:10.1007/978-3-030-04161-8_51 · Cited by 14

Space-time simulation of precipitation based on weather pattern sub-sampling and meta-Gaussian model

Ayar, P. V., Blanchet, J., Paquet, E., & Penot, D. (2019) · Journal of Hydrology · doi:10.1016/j.jhydrol.2019.124451 · Cited by 14

On modeling positive continuous data with spatiotemporal dependence

Bevilacqua, M., Caamaño‐Carrillo, C., & Gaetan, C. (2020) · Environmetrics · doi:10.1002/env.2632 · Cited by 14

Multivariate dependence among cyber risks based on L-hop propagation

Da, G., Xu, M., & Zhao, P. (2021) · Insurance Mathematics and Economics · doi:10.1016/j.insmatheco.2021.09.005 · Cited by 14

Model averaging of machine learning algorithms for digital soil mapping: A minimum variance framework

Bogaert, P., Taghizadeh‐Mehrjardi, R., & Hamzehpour, N. (2023) · Geoderma · doi:10.1016/j.geoderma.2023.116604 · Cited by 14

Correlation-based hierarchical clustering of time series with spatial constraints

Benevento, A., & Durante, F. (2023) · Spatial Statistics · doi:10.1016/j.spasta.2023.100797 · Cited by 14

Multidimensional characterization of particle morphology and mineralogical composition using CT data and R-vine copulas

Furat, O., Kirstein, T., Leißner, T., Bachmann, K., Gutzmer, J., Peuker, U. A., & Schmidt, V. (2023) · Minerals Engineering · doi:10.1016/j.mineng.2023.108520 · Cited by 14

Maintenance decision model for steel bridges: a case in the Netherlands

Attema, T., Kosgodagan, A., Morales‐Nápoles, O., & Maljaars, J. (2016) · Structure and Infrastructure Engineering · doi:10.1080/15732479.2016.1158194 · Cited by 14

Random difference equations with subexponential innovations

Tang, Q., & Yuan, Z. (2016) · Science China Mathematics · doi:10.1007/s11425-016-0146-0 · Cited by 14

The Realized Hierarchical Archimedean Copula in Risk Modelling

Okhrin, O., & Tetereva, A. (2017) · Econometrics · doi:10.3390/econometrics5020026 · Cited by 14

Score-driven copula models for portfolios of two risky assets

Ayala, A. L., & Blazsek, S. (2018) · European Journal of Finance · doi:10.1080/1351847x.2018.1464488 · Cited by 14

Asymptotic independence and support detection techniques for heavy-tailed multivariate data

Lehtomaa, J., & Resnick, S. I. (2020) · Insurance Mathematics and Economics · doi:10.1016/j.insmatheco.2020.05.002 · Cited by 14

Comment on ‘A vine copula mixed effect model for trivariate meta-analysis of diagnostic test accuracy studies accounting for disease prevalence’

Nikoloulopoulos, A. K. (2016) · Statistical Methods in Medical Research · doi:10.1177/0962280216630190 · Cited by 14

Spearman’s footrule and Gini’s gamma: Local bounds for bivariate copulas and the exact region with respect to Blomqvist’s beta

Bukovšek, D. K., Košir, T., Mojškerc, B., & Omladič, M. (2021) · Journal of Computational and Applied Mathematics · doi:10.1016/j.cam.2021.113385 · Cited by 14

Multivariate failure time distributions derived from shared frailty and copulas

Wang, Y., & Emura, T. (2021) · Japanese Journal of Statistics and Data Science · doi:10.1007/s42081-021-00123-1 · Cited by 14

A typical copula is singular

Durante, F., Sánchez, J. F., & Trutschnig, W. (2015) · Journal of Mathematical Analysis and Applications · doi:10.1016/j.jmaa.2015.05.009 · Cited by 14

A comprehensive extension of the FGM copula

Hürlimann, W. (2015) · Statistical Papers · doi:10.1007/s00362-015-0703-1 · Cited by 14

Modelling Volatile Time Series with V-Transforms and Copulas

McNeil, A. J. (2021) · Risks · doi:10.3390/risks9010014 · Cited by 13

Structural reliability analysis of a submerged floating tunnel under copula-based traffic load simulations

Torres-Alves, G. A., Hart, C. M. P. ’., Morales‐Nápoles, O., & Jonkman, S. N. (2022) · Engineering Structures · doi:10.1016/j.engstruct.2022.114752 · Cited by 13

Trivariate frequency analysis of droughts using copulas under future climate change over Vidarbha region in India

Datta, R., & Reddy, M. J. (2023) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-023-02484-3 · Cited by 13

Portfolio optimization based on forecasting models using vine copulas: An empirical assessment for global financial crises

Sahamkhadam, M., & Stephan, A. (2023) · Journal of Forecasting · doi:10.1002/for.3009 · Cited by 13

A Bayesian Vine Algorithm for Geotechnical Site Characterization Using High Dimensional, Multivariate, Limited, and Missing Data

Sharma, A., Zhang, J., & Spagnoli, G. (2024) · Journal of Engineering Mechanics · doi:10.1061/jenmdt.emeng-7460 · Cited by 13

Copula-based transferable models for synthetic population generation

Jutras-Dubé, P., Al-Khasawneh, M. B., Yang, Z., Bas, J., Bastin, F., & Cirillo, C. (2024) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2024.104830 · Cited by 13

Shock models with dependence and asymmetric linkages

Durante, F., Omladič, M., Oražem, L., & Gorenjec, N. R. (2016) · Fuzzy Sets and Systems · doi:10.1016/j.fss.2016.09.006 · Cited by 13

Eliciting Multivariate Uncertainty from Experts: Considerations and Approaches Along the Expert Judgement Process

Werner, C. P., Hanea, A. M., & Morales‐Nápoles, O. (2017) · International series in management science/operations research/International series in operations research & management science · doi:10.1007/978-3-319-65052-4_8 · Cited by 13

Sequential Refined Partitioning for Probabilistic Dependence Assessment

Werner, C. P., Bedford, T., & Quigley, J. M. (2018) · Risk Analysis · doi:10.1111/risa.13162 · Cited by 13

The class of copulas arising from squared distributions: Properties and inference

Quessy, J., & Durocher, M. (2019) · Econometrics and Statistics · doi:10.1016/j.ecosta.2019.02.002 · Cited by 13

Flight-to-quality in the stock–bond return relation: a regime-switching copula approach

Tachibana, M. (2020) · Financial markets and portfolio management · doi:10.1007/s11408-020-00361-5 · Cited by 13

Modeling dynamic dependence between crude oil and natural gas return rates: A time-varying geometric copula approach

Zhang, K., & Zhao, Y. (2020) · Journal of Computational and Applied Mathematics · doi:10.1016/j.cam.2020.113243 · Cited by 13

Nonparametric Copula Estimation for Mixed Insurance Claim Data

Yang, L. (2020) · Journal of Business and Economic Statistics · doi:10.1080/07350015.2020.1835668 · Cited by 13

Selections of vine structures and their applications

Zhu, K. (2022) · Research Repository (Delft University of Technology) · doi:10.4233/uuid:84ba9f5e-2c5c-4280-9789-35fd650fc617

Dependence Modelling in Ultra High Dimensions with Vine Copulas

Czado, C., Müller, D., & Nagler, T. (2018)

Dependence Modeling: Vine Copula Handbook

Kurowicka, D., & Joe, H. (2010) · RePEc: Research Papers in Economics · doi:10.1142/7699 · Cited by 333

Copulas and Dependence Modeling with Examples

Manner, H. (2026) · Contributions to management science · doi:10.1007/978-981-97-9060-9_1

SL-vine and $Δ$-vine copula modelsPreprint

Monggon, T., Sumetkijakan, S., Santiwipanont, T., & Kooakachai, M. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2609.29764