178 citations as recorded by crossref.

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13. Modeling and application of industrial process fault detection based on pruning vine copula J. Wan & S. Li 10.1016/j.chemolab.2018.11.005
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29. A new scheme for multivariate, multisite weather generator with inter-variable, inter-site dependence and inter-annual variability based on empirical copula approach X. Li & V. Babovic 10.1007/s00382-018-4249-5
30. A mutual information based R-vine copula strategy to estimate VaR in high frequency stock market data C. Sharma et al. 10.1371/journal.pone.0253307
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34. An Upper Limit to Youth Psychotherapy Benefit? A Meta-Analytic Copula Approach to Psychotherapy Outcomes P. Jones et al. 10.1177/2167702619858424
35. Is southwestern China experiencing more frequent precipitation extremes? M. Liu et al. 10.1088/1748-9326/9/6/064002
36. Quantifying the joint distribution of drought indicators in Borneo fire-prone area M. Najib et al. 10.1088/1755-1315/880/1/012002
37. Copula-based approximation of particle breakage as link between DEM and PBM A. Spettl et al. 10.1016/j.compchemeng.2017.01.023
38. Vegetation and climate history in the Westeifel Volcanic Field (Germany) during the past 11 000 years based on annually laminated lacustrine maar sediments T. LITT et al. 10.1111/j.1502-3885.2009.00096.x
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42. Evaluation of the ability of regional climate models and a statistical model to represent the spatial characteristics of extreme precipitation L. Yang et al. 10.1002/joc.6602
43. Seasonal copula models for the analysis of glacier discharge at King George Island, Antarctica M. Gómez et al. 10.1007/s00477-016-1217-7
44. Probabilistic spatial prediction of categorical data using elliptical copulas X. Huang & Z. Wang 10.1007/s00477-017-1485-x
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46. Projecting changes in regional temperature and precipitation extremes in the United States J. Schoof & S. Robeson 10.1016/j.wace.2015.09.004
47. Multivariate bias adjustment of high-dimensional climate simulations: the Rank Resampling for Distributions and Dependences (R<sup>2</sup>D<sup>2</sup>) bias correction M. Vrac 10.5194/hess-22-3175-2018
48. Joint probability risk modelling of storm surge and cyclone wind along the coast of Bay of Bengal using a statistical copula N. Bushra et al. 10.1002/joc.6068
49. An Examination of Tail Dependence in Bordeaux Futures Prices and Parker Ratings D. Cyr et al. 10.1017/jwe.2017.18
50. Joint probability-based classifier based on vine copula method for land use classification of multispectral remote sensing data Y. Zhang et al. 10.1007/s12145-020-00487-0
51. Evaluating the effects of climate extremes on crop yield, production and price using multivariate distributions: A new copula application F. Alidoost et al. 10.1016/j.wace.2019.100227
52. Minimum temperature mapping with spatial copula interpolation P. Bostan et al. 10.1016/j.spasta.2020.100464
53. Stochastic Response Assessment of Cross-Sea Bridges under Correlated Wind and Waves via Machine Learning C. Fang et al. 10.1061/(ASCE)BE.1943-5592.0001554
54. Measured and modeled spatial rain rate correlation to improve the prediction method for satellite site diversity T. Tjelta et al. 10.1002/2014RS005476
55. Vulnerability of hop‐yields due to compound drought and heat events over European key‐hop regions V. Potopová et al. 10.1002/joc.6836
56. Copula-based stochastic uncertainty analysis of satellite precipitation products E. Sharifi et al. 10.1016/j.jhydrol.2019.01.035
57. Climate derivatives strategies as an alternative to set up guaranteed prices for agricultural producers in México S. Cruz-Aké et al. 10.1080/13504851.2021.1985061
58. A Similarity-Based Implementation of the Schaake Shuffle R. Schefzik 10.1175/MWR-D-15-0227.1
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61. Assessing Hydrologic Uncertainty Processor Performance for Flood Forecasting in a Semiurban Watershed S. Han et al. 10.1061/(ASCE)HE.1943-5584.0001828
62. Investigation of multivariate pairs trading under copula approach with mixture distribution F. He et al. 10.1016/j.amc.2024.128635
63. The distribution of Kendall's tau for testing the significance of cross-correlation in persistent data K. Hamed 10.1080/02626667.2011.586948
64. Ensemble Model Output Statistics for Wind Vectors N. Schuhen et al. 10.1175/MWR-D-12-00028.1
65. Analysis of extreme precipitation characteristics in low mountain areas based on three-dimensional copulas—taking Kuandian County as an example C. Wang et al. 10.1007/s00704-015-1692-7
66. Interdependence between temperature and precipitation: modeling using copula method toward climate protection B. Hussain et al. 10.1007/s40808-021-01256-8
67. Joint Modeling of Precipitation and Temperature Using Copula Theory for Current and Future Prediction under Climate Change Scenarios in Arid Lands (Case Study, Kerman Province, Iran) T. Mesbahzadeh et al. 10.1155/2019/6848049
68. A physiographic approach to downscaling fractional snow cover data in mountainous regions R. Walters et al. 10.1016/j.rse.2014.07.001
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71. Spatial extreme modeling using student t copula approach in Ngawi Regency M. Fauziyah et al. 10.1088/1742-6596/1538/1/012051
72. Developing an earthquake model based on simultaneous peak ground acceleration occurrences using the D-vine copula approach A. Ahdika et al. 10.1007/s40808-023-01846-8
73. Heterogeneous spatiotemporal copula‐based kriging for air pollution prediction J. Wang et al. 10.1111/tgis.12823
74. Selecting and estimating regular vine copulae and application to financial returns J. Dißmann et al. 10.1016/j.csda.2012.08.010
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76. Analysis of Reserves in Multiple Life Insurance using Copula I. Lee et al. 10.5351/CSAM.2014.21.1.023
77. Entropy–Copula in Hydrology and Climatology A. AghaKouchak 10.1175/JHM-D-13-0207.1
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82. Statistical modelling of extreme storms using copulas: A comparison study F. Li et al. 10.1016/j.coastaleng.2018.09.007
83. Decentralized monitoring for large‐scale process using copula‐correlation analysis and Bayesian inference–based multiblock principal component analysis Y. Tian et al. 10.1002/cem.3158
84. Construction of combined drought index based on bivariate joint distribution Y. Li et al. 10.1016/j.aej.2021.01.006
85. Using Vine Copulas to Generate Representative System States for Machine Learning I. Konstantelos et al. 10.1109/TPWRS.2018.2859367
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87. Functional Enrichment of Utopian Distribution of Plant Life-Forms J. Furze et al. 10.4236/ajps.2013.412A1006
88. Impact of bias nonstationarity on the performance of uni- and multivariate bias-adjusting methods: a case study on data from Uccle, Belgium J. Van de Velde et al. 10.5194/hess-26-2319-2022
89. Dynamic Copula Processes A. Galli et al. 10.2139/ssrn.1616503
90. Mixture of D-vine copulas for modeling dependence D. Kim et al. 10.1016/j.csda.2013.02.018
91. Virtual Storage-Based Model for Estimation of Economic Benefits of Electric Vehicles in Renewable Portfolios J. Vasilj et al. 10.3390/en13092315
92. Evaluating the Dependence between Temperature and Precipitation to Better Estimate the Risks of Concurrent Extreme Weather Events H. Wazneh et al. 10.1155/2020/8763631
93. Joint statistical models for significant wave height and wave period in a changing climate E. Vanem 10.1016/j.marstruc.2016.06.001
94. Multisite and multivariable statistical downscaling using a Gaussian copula quantile regression model M. Ben Alaya et al. 10.1007/s00382-015-2908-3
95. Nonlinear time series analysis of palaeoclimate proxy records N. Marwan et al. 10.1016/j.quascirev.2021.107245
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97. Parametric models for distributions when interest is in extremes with an application to daily temperature M. Stein 10.1007/s10687-020-00378-z
98. Conditional simulation of remotely sensed rainfall data using a non-Gaussian v-transformed copula A. AghaKouchak et al. 10.1016/j.advwatres.2010.02.010
99. Assessing probabilistic wind load effects via a multivariate extreme wind speed model: A unified framework to consider directionality and uncertainty X. Zhang & X. Chen 10.1016/j.jweia.2015.09.002
100. Probabilistic Gaussian Copula Regression Model for Multisite and Multivariable Downscaling M. Ben Alaya et al. 10.1175/JCLI-D-13-00333.1
101. The spatial structure of European wind storms as characterized by bivariate extreme-value Copulas A. Bonazzi et al. 10.5194/nhess-12-1769-2012
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110. Implicit Copulas: An Overview M. Smith 10.1016/j.ecosta.2021.12.002
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126. Multivariate ensemble Model Output Statistics using empirical copulas D. Wilks 10.1002/qj.2414
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141. Bivariate return periods of temperature and precipitation explain a large fraction of European crop yields J. Zscheischler et al. 10.5194/bg-14-3309-2017
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147. Copula-based joint distribution analysis of the ENSO effect on the drought indicators over Borneo fire-prone areas M. Najib et al. 10.1007/s40808-021-01267-5
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150. Accounting for correlational structures in stochastic comparative life cycle assessments through copula modeling R. Gil et al. 10.1007/s11367-020-01859-w
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