Results 1 to 10 of about 97 (72)
Implementing Markovian models for extendible Marshall–Olkin distributions
Dependence Modeling, 2022 We derive a novel stochastic representation of exchangeable Marshall–Olkin distributions based on their death-counting processes. We show that these processes are Markov.
Sloot Henrik
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On copulas of self-similar Ito processes
Dependence Modeling, 2021 We characterize the cumulative distribution functions and copulas of two-dimensional self-similar Ito processes, with randomly correlated Wiener margins, as solutions of certain elliptic partial differential equations.
Jaworski Piotr, Krzywda Marcin
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Generating unfavourable VaR scenarios under Solvency II with patchwork copulas
Dependence Modeling, 2021 The central idea of the paper is to present a general simple patchwork construction principle for multivariate copulas that create unfavourable VaR (i.e. Value at Risk) scenarios while maintaining given marginal distributions.
Pfeifer Dietmar, Ragulina Olena
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On a general class of gamma based copulas
Dependence Modeling, 2021 A large family of copulas with gamma components is examined, and interesting submodels are defined and analyzed. Parameter estimation is demonstrated for some of these submodels. A brief discussion of higher-dimensional versions is included.
Arnold Barry C., Arvanitis Matthew
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The efficiency comparisons between OLSE and BLUE in a singular linear model
Journal of Inequalities and Applications, 2013 This paper is mainly concerned with the efficiency comparison between OLSE and BLUE in a singular linear model. We define the efficiencies between OLSE and BLUE by means of the matrix Euclidean norm and prove a matrix Euclidean norm version of the ...
Litong Wang, Guobing Pan
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A closed-form universal trivariate pair-copula
Journal of Statistical Distributions and Applications, 2014 Based on the trivariate pair-copula construction for the bivariate linear circular copula by Perlman and Wellner (Symmetry 3:574-99, 2011) and the Theorem of Carathéodory, which states that any valid correlation matrix is a finite convex combination of ...
W. Hürlimann
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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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A new proof of fractional Hu-Meyer formula and its applications
Journal of Inequalities and Applications, 2012 This paper is concerned with the Hu-Meyer formula for fractional Brownian motion with the Hurst parameter less than 1/2. By the mollifier approximation, the Hu-Meyer formula is explicitly obtained based on the multiple Stratonovich integral, and the ...
Baobin Wang, Ting Hu
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Multivariate radial symmetry of copula functions: finite sample comparison in the i.i.d case
Dependence Modeling, 2021 Given a d-dimensional random vector X = (X1, . . ., Xd), if the standard uniform vector U obtained by the component-wise probability integral transform (PIT) of X has the same distribution of its point reflection through the center of the unit hypercube,
Billio Monica +2 more
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Explaining predictive models using Shapley values and non-parametric vine copulas
Dependence Modeling, 2021 In this paper the goal is to explain predictions from complex machine learning models. One method that has become very popular during the last few years is Shapley values.
Aas Kjersti +3 more
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