Dependence Modeling with Copulas

Reliability analysis of flood defenses: The case of the Nezahualcoyotl dike in the aztec city of Tenochtitlan

Torres-Alves, G. A., & Morales‐Nápoles, O. (2020) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2020.107057 · Cited by 26

Model‐based inference of conditional extreme value distributions with hydrological applications

Towe, R., Tawn, J. A., Lamb, R., & Sherlock, C. (2019) · Environmetrics · doi:10.1002/env.2575 · Cited by 25

Centering in Multiple Regression Does Not Always Reduce Multicollinearity: How to Tell When Your Estimates Will Not Benefit From Centering

Astivia, O. L. O., & Kroc, E. (2018) · Educational and Psychological Measurement · doi:10.1177/0013164418817801 · Cited by 25

On the quantification and efficient propagation of imprecise probabilities with copula dependence

Zhang, J., & Shields, M. D. (2020) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2020.04.002 · Cited by 25

Explicit Demand Response Potential in Electric Vehicle Charging Networks: Event-Based Simulation Based on the Multivariate Copula Procedure

Einolander, J., & Lahdelma, R. (2022) · Energy · doi:10.1016/j.energy.2022.124656 · Cited by 25

Comparison between canonical vine copulas and a meta-Gaussian model for forecasting agricultural drought over China

Wu, H., Su, X., Singh, V. P., Zhang, T., Qi, J., & Huang, S. (2022) · Hydrology and earth system sciences · doi:10.5194/hess-26-3847-2022 · Cited by 25

Stochastic simulation of velocity pulses of near-fault ground motions based on multivariate copula modeling

Peng, Y., Luo, C., Qiu, C., & Kong, F. (2023) · Probabilistic Engineering Mechanics · doi:10.1016/j.probengmech.2023.103434 · Cited by 25

Estimating bridge criticality due to extreme traffic loads in highway networks

Mendoza‐Lugo, M. A., Nogal, M., & Morales‐Nápoles, O. (2023) · Engineering Structures · doi:10.1016/j.engstruct.2023.117172 · Cited by 25

Toward a New Generation of Log Pre-processing Methods for Process Mining

Ceravolo, P., Damiani, E., Torabi, M., & Barbon, S. (2017) · Lecture notes in business information processing · doi:10.1007/978-3-319-65015-9_4 · Cited by 25

Multiterminal Source Coding With Copula Regression for Wireless Sensor Networks Gathering Diverse Data

Zimos, E., Toumpakaris, D., Munteanu, A., & Deligiannis, N. (2016) · IEEE Sensors Journal · doi:10.1109/jsen.2016.2585042 · Cited by 24

Toward a flexible scenario generation tool for stochastic renewable energy analysis

Wang, T., Chiang, H., & Tanabe, R. (2016) · Power Systems Computation Conference · doi:10.1109/pscc.2016.7540991 · Cited by 24

Compound Effects of Flood Drivers, Sea Level Rise, and Dredging Protocols on Vessel Navigability and Wetland Inundation Dynamics

Muñoz, D. F., Moftakhari, H. R., Kumar, M., & Moradkhani, H. (2022) · Frontiers in Marine Science · doi:10.3389/fmars.2022.906376 · Cited by 24

Dependent risk models with Archimedean copulas: A computational strategy based on common mixtures and applications

Cossette, H., Marceau, É., Mtalai, I., & Veilleux, D. (2017) · Insurance Mathematics and Economics · doi:10.1016/j.insmatheco.2017.11.002 · Cited by 24

Bivariate Seasonal Design Flood Estimation Based on Copulas

Yin, J., Guo, S., Liu, Z., Chen, K., Chang, F., & Xiong, F. (2017) · Journal of Hydrologic Engineering · doi:10.1061/(asce)he.1943-5584.0001594 · Cited by 24

On multivariate asymmetric dependence using multivariate skew-normal copula-based regression

Wei, Z., & Kim, D. (2017) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2017.10.016 · Cited by 24

A unified test for predictability of asset returns regardless of properties of predicting variables

Liu, X., Yang, B., Cai, Z., & Peng, L. (2018) · Journal of Econometrics · doi:10.1016/j.jeconom.2018.09.009 · Cited by 24

Measuring co-dependencies of economic policy uncertainty in Latin American countries using vine copulas

Çekin, S. E., Pradhan, A. K., Tiwari, A. K., & Gupta, R. (2019) · The Quarterly Review of Economics and Finance · doi:10.1016/j.qref.2019.07.004 · Cited by 24

Flexible dynamic vine copula models for multivariate time series data

Acar, E. F., Czado, C., & Lysy, M. (2019) · Econometrics and Statistics · doi:10.1016/j.ecosta.2019.03.002 · Cited by 24

Probabilistic hazard assessment of contaminated sediment in rivers

Shojaeezadeh, S. A., Nikoo, M. R., Mirchi, A., Mallakpour, I., AghaKouchak, A., & Sadegh, M. (2019) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2019.134875 · Cited by 24

Compatible matrices of Spearman’s rank correlation

Wang, B., Wang, R., & Wang, Y. (2019) · Statistics & Probability Letters · doi:10.1016/j.spl.2019.03.015 · Cited by 23

Modeling nonstationary temperature maxima based on extremal dependence changing with event magnitude

Zhong, P., Huser, R., & Opitz, T. (2022) · The Annals of Applied Statistics · doi:10.1214/21-aoas1504 · Cited by 23

Comparison of Value at Risk (VaR) Multivariate Forecast Models

Müller, F. M., & Righi, M. B. (2022) · Computational Economics · doi:10.1007/s10614-022-10330-x · Cited by 23

On the size of the class of bivariate extreme-value copulas with a fixed value of Spearman's rho or Kendall's tau

Kamnitui, N., Genest, C., Jaworski, P., & Trutschnig, W. (2018) · Journal of Mathematical Analysis and Applications · doi:10.1016/j.jmaa.2018.11.057 · Cited by 23

Copula-based joint modeling of crash count and conflict risk measures with accommodation of mixed count-continuous margins

Yang, D., Xie, K., Özbay, K. M. A., Zhao, Z., & Yang, H. J. (2021) · Analytic Methods in Accident Research · doi:10.1016/j.amar.2021.100162 · Cited by 23

Effective estimation algorithm for parameters of multivariate Farlie–Gumbel–Morgenstern copula

Ota, S., & Kimura, M. (2021) · Japanese Journal of Statistics and Data Science · doi:10.1007/s42081-021-00118-y · Cited by 23

Nonparametric C- and D-vine-based quantile regression

Tepegjozova, M., Zhou, J., Claeskens, G., & Czado, C. (2022) · Dependence Modeling · doi:10.1515/demo-2022-0100 · Cited by 22

Dynamic relationship between Stock and Bond returns: A GAS MIDAS copula approach

Nguyen, H., & Javed, F. (2023) · Journal of Empirical Finance · doi:10.1016/j.jempfin.2023.07.004 · Cited by 22

Semiparametric copula quantile regression for complete or censored data

Backer, M. D., Ghouch, A. E., & Keilegom, I. V. (2017) · Electronic Journal of Statistics · doi:10.1214/17-ejs1273 · Cited by 22

Multiple cyber attacks against a target with observation errors and dependent outcomes: Characterization and optimization

Hu, X., Xu, M., Xu, S., & Zhao, P. (2016) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2016.10.025 · Cited by 22

On Conditional Value at Risk (CoVaR) for tail-dependent copulas

Jaworski, P. (2017) · Dependence Modeling · doi:10.1515/demo-2017-0001 · Cited by 22

Multivariate geometric expectiles

Herrmann, K., Hofert, M., & Mailhot, M. (2018) · Scandinavian Actuarial Journal · doi:10.1080/03461238.2018.1426038 · Cited by 22

Copulae in High Dimensions: An Introduction

Okhrin, O., Ristig, A., & Xu, Y. (2017) · Statisctics and computing/Statistics and computing · doi:10.1007/978-3-662-54486-0_13 · Cited by 22

Hazard Assessment under Multivariate Distributional Change-Points: Guidelines and a Flood Case Study

Salvadori, G., Durante, F., Michele, C. D., & Bernardi, M. (2018) · Water · doi:10.3390/w10060751 · Cited by 22

On Identification and Non-normal Simulation in Ordinal Covariance and Item Response Models

Foldnes, N., & Grønneberg, S. (2019) · Psychometrika · doi:10.1007/s11336-019-09688-z · Cited by 22

Regression for copula-linked compound distributions with applications in modeling aggregate insurance claims

Shi, P., & Zhao, Z. (2020) · The Annals of Applied Statistics · doi:10.1214/19-aoas1299 · Cited by 22

Bivariate lifetime models in presence of cure fraction: a comparative study with many different copula functions

Peres, M. V. D. O., Achcar, J. A., & Martínez, E. Z. (2020) · Heliyon · doi:10.1016/j.heliyon.2020.e03961 · Cited by 22

Reliability assessment of marine structures considering multidimensional dependency of the variables

Okoro, A., Khan, F. I., & Ahmed, S. (2021) · Ocean Engineering · doi:10.1016/j.oceaneng.2021.109021 · Cited by 22

Exploring Okun's law asymmetry: An endogenous threshold logistic smooth transition regression approach*

Christopoulos, D., McAdam, P., & Tzavalis, E. (2022) · Oxford Bulletin of Economics and Statistics · doi:10.1111/obes.12515 · Cited by 22

Vulnerability-CoVaR: investigating the crypto-market

Waltz, M., Singh, A. K., & Okhrin, O. (2022) · Quantitative Finance · doi:10.1080/14697688.2022.2063166 · Cited by 21

Spatial and temporal patterns of urban air pollution in tehran with a focus on PM2.5 and associated pollutants

Abbasi, M. T., Alesheikh, A. A., Jafari, A., & Lotfata, A. (2024) · Scientific Reports · doi:10.1038/s41598-024-75678-6 · Cited by 21

On tail dependence coefficients of transformed multivariate Archimedean copulas

Bernardino, É. D., & Rullière, D. (2015) · Fuzzy Sets and Systems · doi:10.1016/j.fss.2015.08.030 · Cited by 21

Modeling of Future Extreme Storm Surges at the NW Mediterranean Coast (Spain)

Lin-Ye, J., García-León, M., Gràcia, V., Ortego, M. I., Lionello, P., Conte, D., Pérez-Gómez, B., & Sánchez‐Arcilla, A. (2020) · Water · doi:10.3390/w12020472 · Cited by 21

The Role of Item Distributions on Reliability Estimation: The Case of Cronbach’s Coefficient Alpha

Astivia, O. L. O., Kroc, E., & Zumbo, B. D. (2020) · Educational and Psychological Measurement · doi:10.1177/0013164420903770 · Cited by 21

Copula-based Markov zero-inflated count time series models with application

Alqawba, M., & Diawara, N. (2020) · Journal of Applied Statistics · doi:10.1080/02664763.2020.1748581 · Cited by 21

Streamflow estimation at partially gaged sites using multiple-dependence conditions via vine copulas

Ahn, K. (2021) · Hydrology and earth system sciences · doi:10.5194/hess-25-4319-2021 · Cited by 21

Measuring the bullwhip effect with market competition among retailers: A simulation study

Yin, X. (2021) · Computers & Operations Research · doi:10.1016/j.cor.2021.105341 · Cited by 21

Tracing Uncertainty Contributors in the Multi‐Hazard Risk Analysis for Compound Extremes

Fan, Y., Yu, L., Shi, X., & Duan, Q. (2021) · Earth s Future · doi:10.1029/2021ef002280 · Cited by 21

Football tracking data: a copula-based hidden Markov model for classification of tactics in football

Ötting, M., & Karlis, D. (2022) · Annals of Operations Research · doi:10.1007/s10479-022-04660-0 · Cited by 21

Climate change - induced hazards on touristic island beaches: Cyprus, Eastern Mediterranean

Monioudi, I. Ν., Velegrakis, A. F., Chatzistratis, D., Vousdoukas, M. I., Savva, C., Wang, D., Bové, G., Mentaschi, L., Paprotny, D., Morales‐Nápoles, O., et al. (2023) · Frontiers in Marine Science · doi:10.3389/fmars.2023.1188896 · Cited by 21

Quasi-random numbers for copula models

Cambou, M., Hofert, M., & Lemieux, C. (2016) · Statistics and Computing · doi:10.1007/s11222-016-9688-4 · Cited by 21

Bayesian Non-Parametric Conditional Copula Estimation of Twin Data

Valle, L. D., Leisen, F., & Rossini, L. (2017) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1111/rssc.12237 · Cited by 21

Conditional risk based on multivariate hazard scenarios

Bernardi, M., Durante, F., Jaworski, P., Petrella, L., & Salvadori, G. (2017) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-017-1425-9 · Cited by 21

A distributional multivariate approach for assessing performance of climate-hydrology models

Vezzoli, R., Salvadori, G., & Michele, C. D. (2017) · Scientific Reports · doi:10.1038/s41598-017-12343-1 · Cited by 21

Conditional quantiles and tail dependence in the volatilities of gold and silver

Bouri, E. I., & Jalkh, N. (2018) · International Economics · doi:10.1016/j.inteco.2018.10.001 · Cited by 21

Within Reach? Habitat Availability as a Function of Individual Mobility and Spatial Structuring

Matthiopoulos, J., Fieberg, J., Aarts, G. M., Barraquand, F., & Kendall, B. E. (2020) · The American Naturalist · doi:10.1086/708519 · Cited by 21

Flexible copula models with dynamic dependence and application to financial data

Krupskii, P., & Joe, H. (2020) · Econometrics and Statistics · doi:10.1016/j.ecosta.2020.01.005 · Cited by 21

Portfolio optimisation under flexible dynamic dependence modelling

Bernardi, M., & Catania, L. (2018) · Journal of Empirical Finance · doi:10.1016/j.jempfin.2018.05.002 · Cited by 21

Statistical Significance Testing in Information Retrieval: An Empirical Analysis of Type I, Type II and Type III Errors

Urbano, J., Lima, H., & Hanjalić, A. (2019) · Research Repository (Delft University of Technology) · doi:10.48550/arxiv.1905.11096 · Cited by 20

Modeling risk dependence and portfolio VaR forecast through vine copula for cryptocurrencies

Syuhada, K. I. A., & Hakim, A. R. (2020) · PLoS ONE · doi:10.1371/journal.pone.0242102 · Cited by 20

Rare event estimation using stochastic spectral embedding

Wagner, P., Marelli, S., Papaioannou, I., Straub, D., & Sudret, B. (2022) · Structural Safety · doi:10.1016/j.strusafe.2021.102179 · Cited by 20

On New Types of Multivariate Trigonometric Copulas

Chesneau, C. (2021) · AppliedMath · doi:10.3390/appliedmath1010002 · Cited by 20

Compounding Effects of Fluvial Flooding and Storm Tides on Coastal Flooding Risk in the Coastal-Estuarine Region of Southeastern China

Lü, W., Tang, L., Yang, D., Wu, H., & Liu, Z. (2022) · Atmosphere · doi:10.3390/atmos13020238 · Cited by 20

Climate change increased the compound extreme precipitation-flood events in a representative watershed of the Yangtze River Delta, China

He, Y., Wang, Q., Xu, Y., Li, Z., Yuan, J., Lu, M., & Lin, Z. (2022) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-022-02229-8 · Cited by 20

Exploring the joint probability of precipitation and soil moisture over Europe using copulas

Cammalleri, C., Michele, C. D., & Toreti, A. (2024) · Hydrology and earth system sciences · doi:10.5194/hess-28-103-2024 · Cited by 20

Selection of sparse vine copulas in high dimensions with the Lasso

Müller, D., & Czado, C. (2018) · Statistics and Computing · doi:10.1007/s11222-018-9807-5 · Cited by 20

Uncertainty Assessment of Synthetic Design Hydrographs for Gauged and Ungauged Catchments

Brunner, M. I., Sikorska‐Senoner, A. E., Furrer, R., & Favre, A. (2018) · Water Resources Research · doi:10.1002/2017wr021129 · Cited by 20

Ensemble Postprocessing Methods Incorporating Dependence Structures

Schefzik, R., & Möller, A. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-812372-0.00004-2 · Cited by 20

Dissimilarity functions for rank-invariant hierarchical clustering of continuous variables

Fuchs, S., Lascio, F. M. L. D., & Durante, F. (2021) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2021.107201 · Cited by 20

Computationally efficient Bayesian estimation of high-dimensional Archimedean copulas with discrete and mixed margins

Gunawan, D., Tran, M., Suzuki, K., Dick, J., & Kohn, R. (2018) · Statistics and Computing · doi:10.1007/s11222-018-9846-y · Cited by 20

Collective risk models with dependence

Cossette, H., Marceau, É., & Mtalai, I. (2019) · Insurance Mathematics and Economics · doi:10.1016/j.insmatheco.2019.04.008 · Cited by 20

Vine copula mixture models and clustering for non-Gaussian data

Şahin, Ö., & Czado, C. (2021) · Econometrics and Statistics · doi:10.1016/j.ecosta.2021.08.011 · Cited by 20

Some reliability issues for incomplete two-dimensional warranty claims data

Gupta, S. K., De, S., & Chatterjee, A. (2016) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2016.08.015 · Cited by 20

A new mixed MNP model accommodating a variety of dependent non-normal coefficient distributions

Bhat, C. R., & Lavieri, P. S. (2017) · Theory and Decision · doi:10.1007/s11238-017-9638-4 · Cited by 20

Safe-haven and hedge currencies for the US, UK, and Euro area stock markets: A copula-based approach

Tachibana, M. (2017) · Global Finance Journal · doi:10.1016/j.gfj.2017.07.001 · Cited by 20

Copula theory and probabilistic sensitivity analysis: Is there a connection?

Plischke, E., & Borgonovo, E. (2019) · European Journal of Operational Research · doi:10.1016/j.ejor.2019.03.034 · Cited by 20

Copula-based measures of reflection and permutation asymmetry and statistical tests

Krupskii, P. (2016) · Statistical Papers · doi:10.1007/s00362-016-0743-1 · Cited by 19

Extreme sea levels under present and future climate: a pan-European database

Paprotny, D., Morales‐Nápoles, O., & Nikulin, G. (2016) · E3S Web of Conferences · doi:10.1051/e3sconf/20160702001 · Cited by 19

Relative ageing of series and parallel systems: Effects of dependence and heterogeneity among components

Ding, W., & Zhang, Y. (2018) · Operations Research Letters · doi:10.1016/j.orl.2018.01.005 · Cited by 19

A bivariate extension of the beta generated distribution derived from copulas

Samanthi, R. G. M., & Sepanski, J. H. (2018) · Communication in Statistics- Theory and Methods · doi:10.1080/03610926.2018.1429626 · Cited by 19

Bivariate copula function-based spatial–temporal characteristics analysis of drought in Anhui Province, China

Wang, R., Zhao, C., Zhang, J., Guo, E., Li, D., Alu, S., Ha, S., & Dong, Z. (2018) · Meteorology and Atmospheric Physics · doi:10.1007/s00703-018-0640-3 · Cited by 19

Zero-inflated count time series models using Gaussian copula

Alqawba, M., Diawara, N., & Chaganty, N. R. (2019) · Sequential Analysis · doi:10.1080/07474946.2019.1648922 · Cited by 19

Outer power transformations of hierarchical Archimedean copulas: Construction, sampling and estimation

Górecki, J., Hofert, M., & Okhrin, O. (2020) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2020.107109 · Cited by 19

A new approach to interspecific synchrony in population ecology using tail association

Ghosh, S., Sheppard, L. W., Reid, P. C., & Reuman, D. C. (2020) · Ecology and Evolution · doi:10.1002/ece3.6732 · Cited by 19

On studying extreme values and systematic risks with nonlinear time series models and tail dependence measures

Zhang, Z. (2020) · Statistical Theory and Related Fields · doi:10.1080/24754269.2020.1856590 · Cited by 19

Student Performance Predictions for Advanced Engineering Mathematics Course With New Multivariate Copula Models

Nguyen‐Huy, T., Deo, R. C., Khan, S., Devi, A., Adeyinka, A. A., Apan, A., & Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Z. M. (2022) · IEEE Access · doi:10.1109/access.2022.3168322 · Cited by 19

Marshall–Olkin type copulas generated by a global shock

Durante, F., Girard, S., & Mazo, G. (2015) · Journal of Computational and Applied Mathematics · doi:10.1016/j.cam.2015.10.022 · Cited by 19

Estimating Transfer Entropy via Copula EntropyPreprint

Ma, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.04375 · Cited by 19

Hierarchical Archimedean copulas through multivariate compound distributions

Cossette, H., Gadoury, S.-P., Marceau, É., & Mtalai, I. (2017) · Insurance Mathematics and Economics · doi:10.1016/j.insmatheco.2017.06.001 · Cited by 18

Geometry of discrete copulas

Perrone, E., Solus, L., & Uhler, C. (2019) · Journal of Multivariate Analysis · doi:10.1016/j.jmva.2019.01.014 · Cited by 18

Subsampling MCMC - an Introduction for the Survey Statistician

Quiroz, M., Villani, M., Kohn, R., Tran, M., & Dang, K. (2018) · Sankhya A · doi:10.1007/s13171-018-0153-7 · Cited by 18

Risk Measurement of Stock Markets in BRICS, G7, and G20: Vine Copulas versus Factor Copulas

Song, Q., Liu, J., & Sriboonchitta, S. (2019) · Mathematics · doi:10.3390/math7030274 · Cited by 18

Copula-based monitoring schemes for non-Gaussian multivariate processes

Krupskii, P., Harrou, F., Hering, A. S., & Sun, Y. (2019) · Journal of Quality Technology · doi:10.1080/00224065.2019.1571339 · Cited by 18

Univariate Continuous Distributions

Yee, T. W. (2015) · Springer series in statistics · doi:10.1007/978-1-4939-2818-7_12 · Cited by 18

Extensions of subcopulas

de Amo, E., Carrillo, M. D., Durante, F., & Sánchez, J. F. (2017) · Journal of Mathematical Analysis and Applications · doi:10.1016/j.jmaa.2017.02.061 · Cited by 18

Efron’s monotonicity property for measures on R2

Saumard, A., & Wellner, J. A. (2018) · Journal of Multivariate Analysis · doi:10.1016/j.jmva.2018.03.005 · Cited by 18

Relationship between stock and currency markets conditional on the US stock returns: A vine copula approach

Tachibana, M. (2018) · Journal of Multinational Financial Management · doi:10.1016/j.mulfin.2018.05.001 · Cited by 18

Probability distribution for size and mass of a nodule in the KR5 area for the development of a manganese nodule miner

Kim, S., Cho, S., Lim, W., Lee, T. H., Park, S., Hong, S., Kim, H.-W., Min, C.-H., Choi, J.-S., Ko, Y.-T., et al. (2018) · Ocean Engineering · doi:10.1016/j.oceaneng.2018.10.041 · Cited by 18

Variable Selection with Copula EntropyPreprint

Ma, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.12389 · Cited by 18

Land Resilience and Tail Dependence among Crop Yield Distributions

Du, X., Hennessy, D. A., Feng, H., & Arora, G. (2017) · American Journal of Agricultural Economics · doi:10.1093/ajae/aax082 · Cited by 17

A copula‐based approach on optimal allocation of hot standbys in series systems

Zhang, J., & Zhang, Y. (2022) · Naval Research Logistics (NRL) · doi:10.1002/nav.22055 · Cited by 17

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