Results 1 to 10 of about 691 (95)
Fast inference methods for high-dimensional factor copulas
Dependence Modeling, 2022 Gaussian factor models allow the statistician to capture multivariate dependence between variables. However, they are computationally cumbersome in high dimensions and are not able to capture multivariate skewness in the data.
Verhoijsen Alex, Krupskiy Pavel
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A combinatorial proof of the Gaussian product inequality beyond the MTP2 case
Dependence Modeling, 2022 A combinatorial proof of the Gaussian product inequality (GPI) is given under the assumption that each component of a centered Gaussian random vector X=(X1,…,Xd){\boldsymbol{X}}=\left({X}_{1},\ldots ,{X}_{d}) of arbitrary length can be written as a ...
Genest Christian, Ouimet Frédéric
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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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Checkerboard copula defined by sums of random variables
Dependence Modeling, 2020 We consider the problem of finding checkerboard copulas for modeling multivariate distributions. A checkerboard copula is a distribution with a corresponding density defined almost everywhere by a step function on an m-uniform subdivision of the unit ...
Kuzmenko Viktor +2 more
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On a zonal polynomial integral
Journal of Applied Mathematics, Volume 2003, Issue 11, Page 569-573, 2003., 2003 A certain multiple integral occurring in the studies of Beherens‐Fisher multivariate problem has been evaluated by Mathai et al. (1995) in terms of invariant polynomials. However, this paper explicitly evaluates the context integral in terms of zonal polynomials, thus establishing a relationship between zonal polynomial integrals and invariant ...
A. K. Gupta, D. G. Kabe
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Matrix rank and inertia formulas in the analysis of general linear models
Open Mathematics, 2017 Matrix mathematics provides a powerful tool set for addressing statistical problems, in particular, the theory of matrix ranks and inertias has been developed as effective methodology of simplifying various complicated matrix expressions, and ...
Tian Yongge
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New copulas based on general partitions-of-unity and their applications to risk management (part II)
Dependence Modeling, 2017 We present a constructive and self-contained approach to data driven infinite partition-of-unity copulas that were recently introduced in the literature.
Pfeifer Dietmar +2 more
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Dependence Modeling, 2019 A latent class model is proposed to examine couples’ breadwinning typologies and explain the wage differentials according to the socio-demographic characteristics of the society with data collected through surveys.
Pennoni Fulvia, Nakai Miki
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On a class of norms generated by nonnegative integrable distributions
Dependence Modeling, 2019 We show that any distribution function on ℝd with nonnegative, nonzero and integrable marginal distributions can be characterized by a norm on ℝd+1, called F-norm. We characterize the set of F-norms and prove that pointwise convergence of a sequence of F-
Falk Michael, Stupfler Gilles
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Bayesian estimation of generalized partition of unity copulas
Dependence Modeling, 2020 This paper proposes a Bayesian estimation algorithm to estimate Generalized Partition of Unity Copulas (GPUC), a class of nonparametric copulas recently introduced by [18].
Masuhr Andreas, Trede Mark
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