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

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

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

**Similar probability books**

**Probability: Theory and Examples (4th Edition)**

This booklet is an advent to chance idea overlaying legislation of enormous numbers, imperative restrict theorems, random walks, martingales, Markov chains, ergodic theorems, and Brownian movement. it's a accomplished remedy targeting the implications which are the main worthwhile for functions. Its philosophy is that tips to research chance is to determine it in motion, so there are two hundred examples and 450 difficulties.

**Multidimensional Diffusion Processes**

"This ebook is a superb presentation of the applying of martingale conception to the idea of Markov procedures, specifically multidimensional diffusions. This strategy used to be initiated via Stroock and Varadhan of their recognized papers. (. .. ) The proofs and strategies are awarded in this kind of method that an model in different contexts could be simply performed.

- Time Series Analysis, Fourth Edition
- Simulation and the Monte Carlo Method, Second Edition
- Stochastic Equations for Complex Systems: Theoretical and Computational Topics
- A weak convergence approach to the theory of large deviations
- Ecole D'Ete De Probabilites De Saint-Flour V, 1975

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

**Sample text**

Statist. Math. 39, 113-128. Kimeldorf, G. and Sampson, A. R. (1989) A framework for positive dependence, Ann. Inst. Statist. Math. 41, 31-45. Kotz, S. and Johnson, N. L. (1977) Proprietes de dependance des distributions interees generalisees deux variables FarlieGumbel-Morgenstern, C. R. Acad. Sci. Paris 285A, 277-280. Kruskal, W. H. (1958) Ordinal measures of association, J. Amer. Statist. Assoc. 53, 814-861. Lehmann, E. L. (1966) Some concepts of dependence, Ann. Math. Statist. 37, 1137-1153.

To make this more precise, we need the following: Generally 28 B. 1. f. 's defined on a common probability space, such that df(X) = F, =G df(Y) and df(V(X,Y)) = ~(F,G). 1. Min. Then 'T Let T be any (left-continuous) t-norm other than is not derivable from any function on random variables. 6) below. 1) F pr Fpq * q p,q,r lies between of a probabilistic metric p and r if and only if Fqr , and he showed that this relation has all the properties of ordinary metric betweenness. 2) F = ,(F ,F ). pr pq qr is Wald-between p and r if and only if But now the situation is more complicated since an arbitrary triangle function may not possess all the pleasant properties of convolution.

In but it is not hard to V. MEASURES OF DEPENDENCE The results presented in the first part of this paper were all ob- tained in connection with and as offshoots of problems arising in the theory of probabilistic metric spaces. Those of us working on these matters had no formal training in statistics. Thus we were only tan- gentially aware of possible statistical applications. Moreover, with the notable exception of Sklar's original paper [68], our results were presented in a novel context and published in journals not generally read by statisticians.