Results 21 to 30 of about 119 (106)
About the exact simulation of bivariate (reciprocal) Archimax copulas
Dependence Modeling, 2022 We provide an exact simulation algorithm for bivariate Archimax copulas, including instances with negative association. In contrast to existing simulation approaches, the feasibility of our algorithm is directly linked to the availability of an exact ...
Mai Jan-Frederik
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Measures of concordance determined by D4‐invariant copulas
International Journal of Mathematics and Mathematical Sciences, Volume 2004, Issue 70, Page 3867-3875, 2004., 2004 A continuous random vector (X, Y) uniquely determines a copula C : [0, 1] 2 → [0, 1] such that when the distribution functions of X and Y are properly composed into C, the joint distribution function of (X, Y) results. A copula is said to be D4‐invariant if its mass distribution is invariant with respect to the symmetries of the unit square.
H. H. Edwards +2 more
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Dependence modeling in stochastic frontier analysis
Dependence Modeling, 2022 This review covers several of the core methodological and empirical developments surrounding stochastic frontier models that incorporate various new forms of dependence.
Mamonov Mikhail E. +2 more
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Characterizations of multinomial distributions based on conditional distributions
International Journal of Mathematics and Mathematical Sciences, Volume 19, Issue 3, Page 595-602, 1996., 1994 Several characterizations of the joint multinomial distribution of two discrete random vectors are derived assuming conditional multinomial distributions.
Khoan T. Dinh +2 more
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Technical and allocative inefficiency in production systems: a vine copula approach
Dependence Modeling, 2022 Modeling the error terms in stochastic frontier models of production systems requires multivariate distributions with certain characteristics. We argue that canonical vine copulas offer a natural way to model the pairwise dependence between the two main ...
Zhai Jian, James Robert, Prokhorov Artem
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A characterization of matrix variate normal distribution
International Journal of Mathematics and Mathematical Sciences, Volume 17, Issue 2, Page 341-346, 1994., 1993 The joint normality of two random vectors is obtained based on normal conditional with linear regression and constant covariance matrix of each vector given the value of the other without assuming the existence of the joint density. This result is applied to a characterization of matrix variate normal distribution.
Khoan T. Dinh, Truc T. Nguyen
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On partially Schur-constant models and their associated copulas
Dependence Modeling, 2021 Schur-constant vectors are used to model duration phenomena in various areas of economics and statistics. They form a particular class of exchangeable vectors and, as such, rely on a strong property of symmetry.
Lefèvre Claude
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Dispersive order comparisons on extreme order statistics from homogeneous dependent random vectors
Dependence Modeling, 2021 In this paper, we investigate sufficient conditions for preservation property of the dispersive order for the smallest and largest order statistics of homogeneous dependent random vectors.
Mesfioui Mhamed, Trufin Julien
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International Journal of Mathematics and Mathematical Sciences, Volume 14, Issue 1, Page 45-53, 1991., 1990 In this paper we study some functionals operating on the set of the n‐copulas defined on [0, 1] n. Conditions under which such functionals are well defined are determined and some counterexamples are described. The study of the fixed points (n‐copulas) for these functionals is also considered, and, finally, some open problems are presented.
C. Alsina, A. Damas, J. J. Quesada
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Maximum asymmetry of copulas revisited
Dependence Modeling, 2018 Motivated by the nice characterization of copulas A for which d∞(A, At) is maximal as established independently by Nelsen [11] and Klement & Mesiar [7], we study maximum asymmetry with respect to the conditioning-based metric D1 going back to Trutschnig [
Kamnitui Noppadon +2 more
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