Results 11 to 20 of about 132 (111)
Extreme values of random or chaotic discretization steps and connected networks
, 2012 Classification AMS : 60E15; 60F99; 62H05. Consultable en ligne sur le site : http://www.m-hikari.com/ams/ams-2012/ams-117-120-2012/index.htmlBy sorting independent random variables and considering the difference between two consecutive order statistics ...
Guegan, Dominique, Garcin, Matthieu
core +7 more sources
On Robinsonian dissimilarities, the consecutive ones property and latent variable models [PDF]
, 2009 Dissimilarity measures, Binary data, Ordinal comparison, Pyramids, Ordered clustering systems, Weakly pseudo-hierarchies, 62H05, 62H20,
Matthijs J. Warrens +2 more
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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
wiley +1 more source
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
doaj +1 more source
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
wiley +1 more source
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
doaj +1 more source
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
wiley +1 more source
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
wiley +1 more source
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
doaj +1 more source
Dependence Modeling, 2023 The probability integral transform of a continuous random variable XX with distribution function FX{F}_{X} is a uniformly distributed random variable U=FX(X)U={F}_{X}\left(X). We define the angular probability integral transform (APIT) as θU=2πU=2πFX(X){\
Fernández-Durán Juan José +1 more
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