Detecting and modeling critical dependence structures between random inputs of computer models
DOI10.1515/demo-2020-0016zbMath1457.62098arXiv1804.10527OpenAlexW3112461538MaRDI QIDQ828054
Bertrand Michel, Philippe Saint-Pierre, Nazih Benoumechiara, Nicolas Bousquet
Publication date: 14 January 2021
Published in: Dependence Modeling (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1804.10527
Nonparametric regression and quantile regression (62G08) Density estimation (62G07) Measures of association (correlation, canonical correlation, etc.) (62H20) Characterization and structure theory for multivariate probability distributions; copulas (62H05) Applications of statistics in engineering and industry; control charts (62P30) Statistics of extreme values; tail inference (62G32)
Uses Software
Cites Work
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- Pair-copula constructions of multiple dependence
- Selecting and estimating regular vine copulae and application to financial returns
- Uncertainty management in simulation-optimization of complex systems. Algorithms and applications
- Sequential Bayesian model selection of regular vine copulas
- Rates of convergence for robust geometric inference
- The tight constant in the Dvoretzky-Kiefer-Wolfowitz inequality
- An introduction to copulas.
- Multivariate Archimedean copulas, \(d\)-monotone functions and \(\ell _{1}\)-norm symmetric distributions
- Vines -- a new graphical model for dependent random variables.
- Data-driven polynomial chaos expansion for machine learning regression
- Probability density decomposition for conditionally dependent random variables modeled by vines
- Expert elicitation for reliable system design. (With comments and rejoinder)
- Price of Correlations in Stochastic Optimization
- A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code
- Correlations and Copulas for Decision and Risk Analysis
- Truncated regular vines in high dimensions with application to financial data
- Asymptotic Minimax Character of the Sample Distribution Function and of the Classical Multinomial Estimator
- The t Copula and Related Copulas
- Multi-marginal optimal transport: Theory and applications
- Rare Event Simulation using Monte Carlo Methods
- Asymptotic Statistics
- Multivariate extreme‐value distributions with applications to environmental data
- Testing the Gaussian copula hypothesis for financial assets dependences
- One-dimensional empirical measures, order statistics, and Kantorovich transport distances
- Understanding Relationships Using Copulas
- A NEW MEASURE OF RANK CORRELATION
- A Stochastic Approximation Method
- Optimal Transport
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