Results 11 to 20 of about 385 (163)
Lorenz-generated bivariate Archimedean copulas
Dependence Modeling, 2020 A novel generating mechanism for non-strict bivariate Archimedean copulas via the Lorenz curve of a non-negative random variable is proposed. Lorenz curves have been extensively studied in economics and statistics to characterize wealth inequality and ...
Fontanari Andrea +2 more
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Permutation Tests Based on the Copula-Graphic Estimator and Their Use for Survival Tree Construction. [PDF]
Stat MedABSTRACT Survival trees are popular alternatives to Cox or Aalen regression models that offer both modeling flexibility and graphical interpretability. This paper introduces a new algorithm for survival trees that relaxes the assumption of independent censoring. To this end, we use the copula‐graphic estimator to estimate survival functions.
Baur P, Pauly M, Emura T.
europepmc +2 more sources
Causal Inference for First Non-Fatal Events With the Competing Risk of Death: A Principal Stratification Approach. [PDF]
Stat MedABSTRACT In clinical trials involving both mortality and morbidity, an active treatment can influence the observed risk of the first nonfatal event either directly, through its effect on the underlying nonfatal event process, or indirectly, through its effect on the death process, or both.
Sun J, Cook T.
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Convergence of Archimedean copulas [PDF]
Statistics & Probability Letters, 2006 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Charpentier, Arthur, Segers, Johan
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International Journal of Statistics and Probability, 2021 The copula function is an effective and elegant tool useful for modeling dependence between random variables. Among the many families of this function, one of the most prominent family of copula is the Archimedean family, which has its unique structure and features.
Moshe Kelner +2 more
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Time Varying Hierarchical Archimedean Copulae [PDF]
SSRN Electronic Journal, 2010 There is increasing demand for models of time-varying and non-Gaussian dependencies for multivariate time-series. Available models suffer from the curse of dimensionality or restrictive assumptions on the parameters and the distribution. A promising class of models are the hierarchical Archimedean copulae (HAC) that allow for non-exchangeable and non ...
Wolfgang Karl Härdle +2 more
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Structured Expert Elicitation of Dependence Between River Tributaries Using Nonparametric Bayesian Networks. [PDF]
Risk AnalABSTRACT In absence of sufficient data, structured expert judgment is a suitable method to estimate uncertain quantities. While such methods are well established for individual variables, eliciting their dependence in a structured manner is a less explored field of research.
Rongen G +3 more
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Generative Archimedean Copulas
, 2021 We propose a new generative modeling technique for learning multidimensional cumulative distribution functions (CDFs) in the form of copulas. Specifically, we consider certain classes of copulas known as Archimedean and hierarchical Archimedean copulas, popular for their parsimonious representation and ability to model different tail dependencies.
Ng, Yuting +3 more
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Properties of hierarchical Archimedean copulas [PDF]
Statistics & Risk Modeling, 2013 Abstract In this paper we analyse the properties of hierarchical Archimedean copulas. This class is a generalisation of the Archimedean copulas and allows for general non-exchangeable dependency structures. We show that the structure of the copula can be uniquely recovered from all bivariate margins.
Ostap Okhrin +2 more
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Simulation algorithms for hierarchical Archimedean copulas beyond the completely monotone case
Dependence Modeling, 2019 Two simulation algorithms for hierarchical Archimedean copulas in the case when intra-group generators are not necessarily completely monotone are presented. Both generalize existing algorithms for the completely monotone case.
Mai Jan-Frederik
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