Engine remaining useful life prediction model based on R-Vine copula with multi-sensor data. [PDF]
Heliyon, 2023 Liu S, Jiang H.
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Copula Modeling of COVID-19 Excess Mortality
RisksCOVID-19’s effects on mortality are hard to quantify. Issues with attribution can cause problems with resulting conclusions. Analyzing excess mortality addresses this concern and allows for the analysis of broader effects of the pandemic.
Jonas Asplund, Arkady Shemyakin
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Erratum regarding “Optimizing effective numbers of tests by vine copula modeling”
Dependence Modeling, 2020 We correct the definition of the family-wise error rate in our previous article “Optimizing effective numbers of tests by vine copula modeling”.
Steffen Nico, Dickhaus Thorsten
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Mixed vine copula flows for flexible modeling of neural dependencies. [PDF]
Front Neurosci, 2022 Mitskopoulos L, Amvrosiadis T, Onken A.
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Investigation of Corticomuscular Functional Coupling during Hand Movements Using Vine Copula. [PDF]
Brain Sci, 2022 Ye F, Ding J, Chen K, Xi X.
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Random Traffic Flow Simulation of Heavy Vehicles Based on R-Vine Copula Model and Improved Latin Hypercube Sampling Method. [PDF]
Sensors (Basel), 2023 Lu H, Sun D, Hao J.
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D-vine copula based quantile regression and the simplifying assumption for vine copulas [PDF]
, 2017 In the first part of this thesis we propose a novel semiparametric approach to perform quantile regression using D-vine copulas, a subclass of the flexible class of vine copula models. Various applications and the extension to discrete data are presented.
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A D-vine copula-based quantile regression model with spatial dependence for COVID-19 infection rate in Italy. [PDF]
Spat Stat, 2022 D'Urso P, De Giovanni L, Vitale V.
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Contributions to Vine-Copula Modeling
, 2022 Regular vine-copula models (R-vines) are a powerful statistical tool for modeling thedependence structure of multivariate distribution functions. In particular, they allow modelingdierent types of dependencies among random variables independently of their marginaldistributions, which is deemed the most valued characteristic of these models.
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Learning Vine Copula Models for Synthetic Data Generation
Proceedings of the AAAI Conference on Artificial Intelligence, 2019 A vine copula model is a flexible high-dimensional dependence model which uses only bivariate building blocks. However, the number of possible configurations of a vine copula grows exponentially as the number of variables increases, making model selection a major challenge in development.
Sun, Yi +2 more
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