By G. Dall’aglio (auth.), G. Dall’Aglio, S. Kotz, G. Salinetti (eds.)

*As the reader could most likely already finish from the**enthusiastic phrases within the first traces of this evaluation, this publication can be**strongly steered to probabilists and statisticians who deal with**distributions with given marginals.***Mededelingen van het Wiskundig Genootschap**

**Read Online or Download Advances in Probability Distributions with Given Marginals: Beyond the Copulas PDF**

**Best probability books**

**Applied Adaptive Statistical Methods: Tests of Significance and Confidence Intervals**

ASA-SIAM sequence on information and utilized chance 12 Adaptive statistical assessments, built over the past 30 years, are usually extra robust than conventional assessments of value, yet haven't been time-honored. so far, discussions of adaptive statistical tools were scattered around the literature and customarily don't contain the pc courses essential to make those adaptive equipment a pragmatic replacement to standard statistical tools.

- Structural Equations with Latent Variables
- Path Integral Quantization and Stochastic Quantization
- Stochastic systems in merging phase space MVspa
- Probability and potentials , Edition: 1st
- Applied Statistics and Probability for Engineers

**Extra resources for Advances in Probability Distributions with Given Marginals: Beyond the Copulas**

**Sample text**

5. DERIVABILITY AND BOUNDS We conclude the first part of this paper with a discussion of several additional results that were obtained in the 1970's in the course of our work on probabilistic metric spaces. f. f. 's, Y such that (or, as we shall henceforth often write, df(X) df(X + Y) and = F* G. Y such that df(X) df(L(X,Y)) = F, = aC,L. v. 's is the copula of Since the binary operations TT X and X and Y, playa promi- nent role in the theory of probabilistic metric spaces, and since TMin = a Min , it is natural to ask: What binary operations on random variables correspond to these operations?

It Hoeffding's collected papers THIRTY YEARS OF COPULAS 35 Since 1959, copulas have been rediscovered by several authors. first to do so were Kimeldorf and Sampson. 9) to define two-dimensional copulas. They called them uniform representations; and in [35] and several subsequent papers [36, 37] they developed many of their basic properties and used them as a tool to define and study various dependence notions. Further de- tails and additional references may be found in Sampson's contribution to this volume.

For example, if for any a > 0, we let L be defined by L (u,v) a a a (u + va)l/a, then the operations are the a-convolutions C,La which have been extensively studied by K. Urbanik [73]. 13) TT ,L (F ,G)(x) ~+ belonging to yield a two-parameter family of defined on TT,L When restricted to B of L V via sUPL(u,v) = xT(F(u) ,G(v)). n V, this leads to a larger class of triangle functions (see Chapter 7 of [62] for details). J. Frank undertook a detailed study of the operations 0c [15]. From the point of view of probabilistic metric spaces, where we are interested in obtaining a supply of triangle functions, it is natural to ask: When is associative?