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

*As the reader may most likely already finish from the**enthusiastic phrases within the first strains of this evaluation, this e-book can be**strongly urged 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**

**The doctrine of chances; or, A method of calculating the probabilities of events in play**

Within the 12 months 1716 Abraham de Moivre released his Doctrine of possibilities, during which the topic of Mathematical likelihood took a number of lengthy strides ahead. many years later got here his Treatise of Annuities. while the 3rd (and ultimate) version of the Doctrine used to be released in 1756 it seemed in a single quantity including a revised version of the paintings on Annuities.

**Statistical analysis: an interdisciplinary introduction to univariate & multivariate methods**

This is often an improved variation of the author's "Multivariate Statistical research. " two times as lengthy, it comprises the entire fabric in that version, yet has a extra huge therapy of introductory tools, specifically speculation trying out, parameter estimation, and experimental layout. It additionally introduces time sequence research, selection research, and extra complex chance issues (see the accompanying desk of contents).

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

**Example text**

Probabilistic metric spaces were first introduced by K. Menger in 1942 [44]. As defined in the 1958 Comptes Rendus note with Sklar, a probabilistic metric space is an ordered pair and F is a mapping from S x S (S,F), into the space t:,,+ where is a set S of probability dis- . 1) on (_00,00), nondecreasing, and For any pair of points p, q in S is left-continuous F(O) =0, F(oo) = 1}. the distribution function F(p,q) is generally denoted by F and, for any real x, its value F (x) is usupq pq ally interpreted as the probability that the "distance" between p and q is less than (I) F (II) F pq pq The distribution functions x.

C. (1975) Unified large-sample theory of general chi-squared statistics for tests of fit, Ann. of Math. 65, 117-143. Mostert, P. S. and Shields. A. L. (1957) On the structure of semigroups on a compact manifold with boundary, Ann. Statist. 3, 599-616. Moynihan, R. (1978) On TT-semigroups of probability distribution 29. 30.

153, 168-198. Kellerer, H. (1964) Verteilungsfunktionen mit gegebenen Harginalverteilungen, Z. Warsch. Verw. Geb. 3, 247-270. Kimberling, C. H. (1973) Exchangeable events and completely monotonic sequences, Rocky Mountain J. Math. 3, 565-574. Kimberling, C. H. (1974) A probabilistic interpretation of complete monotonicity, Aequationes Math. 10, 152-164. Kimeldorf, G. and Sampson, A. R. (1975) One-parameter families of bivariate distributions with fixed marginals, Commun. Statist. 4, 293-301. Kimeldorf.