Dependence Modeling with Copulas

Cited by (1,000)

Items 401–500 of 1,000

1. Quasi-Random Sampling for Multivariate Distributions via Generative Neural Networks
   Marius Hofert, Avinash Prasad, Mu Zhu · 2021 · Journal of Computational and Graphical Statistics
2. A new comprehensive approach for regional drought monitoring
   Rizwan Niaz, Mohammed M. A. Almazah, Ijaz Hussain, Muhammad Faisal, Afrah Yahya Al-Rezami, Mohammed A. Naser · 2022 · PeerJ
3. The Kendall and Spearman rank correlations of the bivariate skew normal distribution
   Andréas Heinen, Alfonso Valdesogo · 2022 · Scandinavian Journal of Statistics
4. Robust DC optimal power flow with modeling of solar power supply uncertainty via R-vine copulas
   Kevin-Martin Aigner, Peter Schaumann, Freimut von Loeper, Alexander Reid Martin, Volker Schmidt, Frauke Liers · 2022 · Optimization and Engineering
5. Total positivity of copulas from a Markov kernel perspective
   Sebastian Fuchs, Marco Tschimpke · 2022 · Journal of Mathematical Analysis and Applications
6. Dependence Modelling with Regular Vine Copula Models: A Case-Study for Car Crash Simulation Data
   Ulf Schepsmeier, Claudia Czado · 2015 · Journal of the Royal Statistical Society Series C (Applied Statistics)
7. Multivariate copulas on the MCUSUM control chart
   Saowanit Sukparungsee, Sasigarn Kuvattana, Piyapatr Busababodhin, Yupaporn Areepong · 2017 · Cogent Mathematics
8. Bayesian inference for a single factor copula stochastic volatility model using Hamiltonian Monte Carlo
   Alexander Kreuzer, Claudia Czado · 2021 · Econometrics and Statistics
9. Tail densities of skew-elliptical distributions
   Harry Joe, Haijun Li · 2019 · Journal of Multivariate Analysis
10. A copula transformation in multivariate mixed discrete-continuous models
    Jae Youn Ahn, Sebastian Fuchs, Rosy Oh · 2020 · Fuzzy Sets and Systems
11. Spin-off Extreme Value and Archimedean copulas for estimating the bivariate structural risk
    Roberta Pappadà, Elisa Perrone, Fabrizio Durante, Gianfausto Salvadori · 2015 · Stochastic Environmental Research and Risk Assessment
12. Factor copula models for mixed data
    Sayed H. Kadhem, Aristidis K. Nikoloulopoulos · 2021 · British Journal of Mathematical and Statistical Psychology
13. Multivariate Spearman's rho for aggregating ranks using copulas
    Justin Bedő, Cheng Soon Ong · 2014 · arXiv (Cornell University)
14. Impact of climate change on tropical cyclone wind and wave hazards and structural reliability of offshore wind turbines along the southeast coast of China
    C. Sheng, Yubin Zhang, Hai Cheng, Kaoshan Dai · 2025 · Coastal Engineering
15. Modeling Point Referenced Spatial Count Data: A Poisson Process Approach
    Diego Morales-Navarrete, Moreno Bevilacqua, Christian Caamaño‐Carrillo, Luis M. Castro · 2022 · Journal of the American Statistical Association
16. Theoretical Contributions to Three Generalized Versions of the Celebioglu–Cuadras Copula
    Christophe Chesneau · 2023 · Analytics
17. Compound Impact of Storm Surge and Flood Characteristics in Coastal Area Based on Copula
    Zhenglei Zhu, Wei Zhang, Wenjin Zhu · 2024 · Water
18. System reliability analysis of building clusters considering inter-structural seismic demand correlation
    Mengjie Xiang, Meng‐Ze Lyu, Jiaxu Shen, Zekun Xu, Jun Chen · 2024 · Structural Safety
19. Natural and Artificial Intelligence Interactions in Digital Networking: A Multilayer Network Model for Economic Value Creation
    Roberto Moro Visconti · 2024 · Journal of Comprehensive Business Administration Research
20. Integrating Copula-Based Random Forest and Deep Learning Approaches for Analyzing Heterogeneous Treatment Effects in Survival Analysis
    Jong‐Min Kim · 2025 · Mathematics
21. Behavioral Measures of Multisensory Integration: Bounds on Bimodal Detection Probability
    Hans Colonius · 2014 · Brain Topography
22. Multivariate Skew Normal Copula for Asymmetric Dependence: Estimation and Application
    Zheng Wei, Seong-Yong Kim, Boseung Choi, Daeyoung Kim · 2018 · International Journal of Information Technology & Decision Making
23. Modeling asymmetric dependences among multivariate soil data for the geotechnical analysis – The asymmetric copula approach
    Yi Zhang, António Topa Gomes, Michael Beer, Ingo Neumann, Udo Nackenhorst, Chul‐Woo Kim · 2019 · SOILS AND FOUNDATIONS
24. The directional optimal transport
    Marcel Nutz, Ruodu Wang · 2022 · The Annals of Applied Probability
25. Estimating The Binary Endogenous Effect of Insurance on Doctor Visits by Copula-Based Regression Additive Models
    Giampiero Marra, Rosalba Radice, David M. Zimmer · 2020 · Journal of the Royal Statistical Society Series C (Applied Statistics)
26. Bayesian Markov Chain Monte Carlo-Based Copulas: Factoring the Role of Large-Scale Climate Indices in Monthly Flood Prediction
    Thong Nguyen‐Huy, Ravinesh Chand Deo, Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Ramendra Prasad, Shahbaz Mushtaq · 2020 · Springer transactions in civil and environmental engineering
27. Copula stochastic volatility in oil returns: Approximate Bayesian computation with volatility prediction
    Audronė Virbickaitė, M. Concepción Ausín, Pedro Galeano · 2020 · Energy Economics
28. Candidate bus selection for dynamic VAR planning towards voltage stability enhancement considering copula‐based correlation of wind and load uncertainties
    Yuan Chi, Yan Hui Xu, Rui Zhang · 2020 · IET Generation Transmission & Distribution
29. Ordinal sums: From triangular norms to bi- and multivariate copulas
    Fabrizio Durante, Erich Peter Klement, Susanne Saminger‐Platz, Carlo Sempi · 2022 · Fuzzy Sets and Systems
30. Volatility and dependence in energy markets
    Jinan Liu, Apostolos Serletis · 2022 · Journal of Economics and Finance
31. Risk aggregation with FGM copulas
    Christopher Blier-Wong, Hélène Cossette, Étienne Marceau · 2023 · Insurance Mathematics and Economics
32. Multiobjective portfolio optimization: Forecasting and evaluation under investment horizon heterogeneity
    Xingyu Dai, Dongna Zhang, Chi Keung Marco Lau, Qunwei Wang · 2023 · Journal of Forecasting
33. Temperature change-informed future multisite streamflow generation to support water supply vulnerability assessments under climate change
    Sukwang Ji, Kuk‐Hyun Ahn · 2023 · Journal of Hydrology
34. (Re-)Reading Sklar (1959)—A Personal View on Sklar’s Theorem
    Gery Geenens · 2024 · Mathematics
35. Model distances for vine copulas in high dimensions
    Matthias Killiches, Daniel Kraus, Claudia Czado · 2017 · Statistics and Computing
36. A bivariate failure time model with random shocks and mixed effects
    Sophie Mercier, Ha Pham · 2016 · Journal of Multivariate Analysis
37. The accountability imperative for quantifying the uncertainty of emission forecasts: evidence from Mexico
    Daniel Puig, Oswaldo Morales‐Nápoles, Fatemeh Bakhtiari, Gissela Landa · 2017 · Climate Policy
38. Multivariate multiple test procedures based on nonparametric copula estimation
    André Neumann, Taras Bodnar, Dietmar Pfeifer, Thorsten Dickhaus · 2018 · Biometrical Journal
39. A COPULA-BASED APPROACH TO INVESTIGATE VERTICAL SHOCK PRICE TRANSMISSION IN THE ITALIAN HOG MARKET
    Fabian Capitanio, Felice Adinolfi, Barry K. Goodwin, Giorgia Rivieccio · 2019 · New Medit
40. Multivariate L-moment based tests for copula selection, with hydrometeorological applications
    I. Ben Nasr, Fateh Chebana · 2019 · Journal of Hydrology
41. Allocations of cold standbys to series and parallel systems with dependent components
    Xiaoyu Zhang, Yiying Zhang, Rui Fang · 2019 · Applied Stochastic Models in Business and Industry
42. On bivariate Kumaraswamy-distorted copulas
    Ranadeera Gamage Madhuka Samanthi, Jungsywan H. Sepanski · 2020 · Communication in Statistics- Theory and Methods
43. Issues, problems and potential solutions when simulating continuous, non-normal data in the social sciences
    Oscar Lorenzo Olvera Astivia · 2020 · Meta-Psychology
44. New Families of Bivariate Copulas via Unit Lomax Distortion
    Fadal Abdullah Ali Aldhufairi, Ranadeera Gamage Madhuka Samanthi, Jungsywan H. Sepanski · 2020 · Risks
45. Identification of marginal and joint CDFs using bivariate type I interval multiply censored data for RBDO of a pick-up device of a pilot mining robot
    Saekyeol Kim, Su‐gil Cho, Tae Hee Lee, Jong-Su Choi, Sanghyun Park, Sup Hong, Hyung-Woo Kim, Cheon-Hong Min, Young-Tak Ko, Sang-Bum Chi · 2021 · Structural and Multidisciplinary Optimization
46. Copula Models for Sociology: Measures of Dependence and Probabilities for Joint Distributions
    Mike Vuolo · 2015 · Sociological Methods & Research
47. Climate change in asset management of infrastructure: A riskbased methodology applied to disruption of traffic on road networks due to the flooding of tunnels
    Elja Huibregtse, Oswaldo Morales‐Nápoles, Laura Hellebrandt, Dominik Paprotny, Sten de Wit · 2016 · European journal of transport and infrastructure research
48. Multivariate countermonotonicity and the minimal copulas
    Woojoo Lee, Ka Chun Cheung, Jae Youn Ahn · 2016 · Journal of Computational and Applied Mathematics
49. A D-Vine Copula-Based Model for Repeated Measurements Extending Linear Mixed Models with Homogeneous Correlation Structure
    Matthias Killiches, Claudia Czado · 2018 · Biometrics
50. Multivariate Poisson hidden Markov models with a case study of modelling seismicity
    Katerina Orfanogiannaki, Dimitris Karlis · 2018 · Australian & New Zealand Journal of Statistics
51. A bivariate power generalized Weibull distribution: A flexible parametric model for survival analysis
    M. C. Jones, Angela Noufaily, Kevin Burke · 2019 · Statistical Methods in Medical Research
52. Discovering Association with Copula Entropy
    Jian Ma · 2019 · arXiv (Cornell University)
53. ESG, Risk, and (Tail) Dependence
    Karoline Bax, Özge Şahin, Claudia Czado, Sandra Paterlini · 2021 · SSRN Electronic Journal
54. cylcop: An R Package for Circular-Linear Copulae with Angular Symmetry
    Florian H. Hodel, John Fieberg · 2021 · bioRxiv (Cold Spring Harbor Laboratory)
55. Theoretical Study of Some Angle Parameter Trigonometric Copulas
    Christophe Chesneau · 2022 · Modelling—International Open Access Journal of Modelling in Engineering Science
56. A time-varying copula approach for describing seasonality in multivariate ocean data
    Pengfei Ma, Yi Zhang · 2023 · Marine Structures
57. The impact of infrastructure sustainable development efficiency on economies: the case of selected ASEAN countries
    Chukiat Chaiboonsri, Anuphak Saosaovaphak, Paponsun Eakkapun, Soumya Chatter · 2024 · Environment Development and Sustainability
58. Short-Term Photovoltaic Power Probabilistic Forecasting Based on Temporal Decomposition and Vine Copula
    Xinghua Wang, Zilv Li, Chenyang Fu, Xixian Liu, Weikang Yang, Xiangyuan Huang, Longfa Yang, Jianhui Wu, Zhuoli Zhao · 2024 · Sustainability
59. Joint probabilistic modelling and sampling from small data via probabilistic learning on manifolds and decoupled multi-probability density evolution method
    Zhiqiang Wan, Meng‐Ze Lyu, Xu Hong, Yupeng Song, Jianbing Chen, Roger Ghanem · 2025 · Probabilistic Engineering Mechanics
60. A novel model and simulation method for multivariate Gaussian fields involving nonlinear probabilistic dependencies and different variable-wise spatial variabilities
    Meng‐Ze Lyu, Yang-Yi Liu, Jianbing Chen · 2025 · Reliability Engineering & System Safety
61. Influence of storm type on compound flood drivers of a mid-latitude coastal urban environment
    Ziyu Chen, Philip Mark Orton, James F. Booth, Thomas Wahl, Arthur T. DeGaetano, Joel Kaatz, Radley Horton · 2025 · Hydrology and earth system sciences
62. Count copula regression model using generalized beta distribution of the second kind
    Hadi Safari‐Katesari, Samira Zaroudi · 2020 · Statistics in Transition New Series
63. Modelling Fuel Injector Spray Characteristics in Jet Engines by Using Vine Copulas
    Maximilian Coblenz, Simon Holz, Hans‐Jörg Bauer, Oliver Grothe, Rainer Koch · 2020 · Journal of the Royal Statistical Society Series C (Applied Statistics)
64. Joint inference on extreme expectiles for multivariate heavy-tailed distributions
    Simone A. Padoan, Gilles Stupfler · 2022 · Bernoulli
65. Zero‐inflated modeling part II : Zero‐inflated models for complex data structures
    Derek S. Young, Eric S. Roemmele, Xuan Shi · 2020 · Wiley Interdisciplinary Reviews Computational Statistics
66. Estimation method for mixture copula models in hydrological context
    I. Ben Nasr, Fateh Chebana · 2022 · Journal of Hydrology
67. Hedging cryptos with Bitcoin futures
    Francis Liu, Natalie Packham, Meng-Jou Lu, Wolfgang Karl Härdle · 2023 · Quantitative Finance
68. Task-Aware Risk Estimation of Perception Failures for Autonomous Vehicles
    Pasquale Antonante, Sushant Veer, Karen Leung, Xinshuo Weng, Luca Carlone, Marco Pavone · 2023 · Robotics: Science and Systems
69. Multivariate Skew Normal Copula for Non-exchangeable Dependence
    Zheng Wei, Seongyong Kim, Daeyoung Kim · 2016 · Procedia Computer Science
70. Some properties of bivariate Schur-constant distributions
    Bao Quoc Ta, Chung Pham Van · 2017 · Statistics & Probability Letters
71. Supermigrativity of aggregation functions
    Fabrizio Durante, Roberto Ricci · 2017 · Fuzzy Sets and Systems
72. Bernstein copula modeling for 2D discrete fracture network simulations
    F. Mendoza-Torres, Martín A. Díaz-Viera, Arturo Erdely · 2017 · Journal of Petroleum Science and Engineering
73. Approximating multivariate posterior distribution functions from Monte Carlo samples for sequential Bayesian inference
    Bram Thijssen, Lodewyk F.A. Wessels · 2020 · PLoS ONE
74. Constructing copulas from shock models with imprecise distributions
    Matjaž Omladič, Damjan Škulj · 2019 · International Journal of Approximate Reasoning
75. Uncertainty analysis of the relationship between discharge and nitrate concentration in the Lower Illinois River using a copula model
    Daeryong Park, Momcilo Markus, Kichul Jung, Myoung‐Jin Um · 2019 · Journal of Cleaner Production
76. Copula Model Selection for Vehicle Component Failures Based on Warranty Claims
    Kathryn Wifvat, John Kumerow, Arkady E. Shemyakin · 2020 · Risks
77. Hierarchical Archimedean Copulas for MATLAB and Octave: The HACopula Toolbox
    Jan Górecki, Marius Hofert, Martin Holeňa · 2020 · Journal of Statistical Software
78. Statistical Pattern Recognition Using Gaussian Copula
    Sumen Sen, Norou Diawara, Khan M. Iftekharuddin · 2015 · Journal of Statistical Theory and Practice
79. Hybrid copula mixed models for combining case-control and cohort studies in meta-analysis of diagnostic tests
    Aristidis K. Nikoloulopoulos · 2016 · Statistical Methods in Medical Research
80. A classification point-of-view about conditional Kendall’s tau
    Alexis Derumigny, Jean‐David Fermanian · 2019 · Computational Statistics & Data Analysis
81. Multidimensional Poverty Index with Dependence-Based Weights
    Kateryna Tkach, Chiara Gigliarano · 2020 · Social Indicators Research
82. Mixed Marginal Copula Modeling
    David Gunawan, Mohamad A. Khaled, Robert Kohn · 2018 · Journal of Business and Economic Statistics
83. Copula-based synthetic data generation for machine learning emulators in weather and climate: application to a simple radiation model
    David Meyer, Thomas Nagler, Robin James Hogan · 2021
84. Dependence modeling of multivariate longitudinal hybrid insurance data with dropout
    Frees, Edward W., Bolancé, Catalina, Guillen, Montserrat, Valdez, Emiliano A. · 2021 · Expert Systems with Applications
85. On a bivariate copula for modeling negative dependence: application to New York air quality data
    Shyamal Ghosh, Prajamitra Bhuyan, Maxim S. Finkelstein · 2022 · Statistical Methods & Applications
86. Hotspot movement of compound events on the Europe continent
    Smit Chetan Doshi, Gerrit Lohmann, Monica Ionita-Scholz · 2023 · Scientific Reports
87. Multivariate joint distribution of five-dimensional wind and wave parameters in the sea-crossing bridge region using Hierarchical Archimedean Copulas
    Yi Yang, Chen Fang, Yongle Li, Cheng Xu, Zhanhui Liu · 2024 · Journal of Wind Engineering and Industrial Aerodynamics
88. A copula-based Gaussian mixture closure method for stochastic response of nonlinear dynamic systems
    Ruijing Zhang, Liang Li, Hongzhe Dai · 2019 · Probabilistic Engineering Mechanics
89. Knowledge Learning of Insurance Risks Using Dependence Models
    Zifeng Zhao, Peng Shi, Xiaoping Feng · 2020 · INFORMS journal on computing
90. The Importance of Thinking Multivariately When Setting Subscale Cutoff Scores
    Edward Kroc, Oscar Lorenzo Olvera Astivia · 2021 · Educational and Psychological Measurement
91. Multi-agent Imitation Learning with Copulas
    Hongwei Wang, Lantao Yu, Zhangjie Cao, Stefano Ermon · 2021 · Lecture notes in computer science
92. Modeling stock-oil co-dependence with Dynamic Stochastic MIDAS Copula models
    Hoang Nguyen, Audronė Virbickaitė · 2023 · Energy Economics
93. Copula-based risk aggregation with trapped ion quantum computers
    Daiwei Zhu, Weiwei Shen, Annarita Giani, Saikat Ray-Majumder, V.B. Neculaes, Sonika Johri · 2023 · Scientific Reports
94. What drives the joint demand for ride-hailing and carsharing services? Understanding consumers’ behaviors, attitudes, & concerns
    Behram Wali · 2023 · Transportation Research Part C: Emerging Technologies
95. Enhanced pricing and management of bundled insurance risks with dependence-aware prediction using pair copula construction
    Peng Shi, Zifeng Zhao · 2024 · Journal of Econometrics
96. High-dimensional sparse vine copula regression with application to genomic prediction
    Özge Şahin, Claudia Czado · 2024 · Biometrics
97. Large Skew-t Copula Models and Asymmetric Dependence in Intraday Equity Returns
    Lin Deng, Michael Stanley Smith, Worapree Ole Maneesoonthorn · 2024 · Journal of Business and Economic Statistics
98. Multivariate analysis of compound hail, wind and rainfall extremes in Alberta's hail alley
    Issam Mohamed, Mohammad Reza Najafi, Paul I. Joe, Julian Brimelow · 2024 · Weather and Climate Extremes
99. Vine copulas for mixed data : multi-view clustering for mixed data beyond meta-Gaussian dependencies
    Lavanya Sita Tekumalla, Vaibhav Rajan, Chiranjib Bhattacharyya · 2017 · Machine Learning
100. A D-vine copula mixed model for joint meta-analysis and comparison of diagnostic tests
     Aristidis K. Nikoloulopoulos · 2018 · Statistical Methods in Medical Research