This paper deals with the validity in fuzzy logic of some classical schemes of reasoning, namely, with those of disjunctive reasoning, resolution, reductio ad absurdum, and the so-called constructive dilemma.

We study a method, which we call a copula (or quasi-copula) diagonal splice, for creating new functions by joining portions of two copulas (or quasi-copulas) with a common diagonal section. The diagonal splice of two quasi-copulas is always a quasi-copula, and we find a necessary and sufficient...

This paper studies the problem of finding best-possible upper bounds on the Value-at-Risk for a function of two random variables when the marginal distributions are known and additional nonparametric information on the dependence structure, such as the value of a measure of association, is...

Using the technique of finding bounds on sets of copulas with particular properties, we compare the distribution of an n-dimensional (n≥3) vector of continuous pairwise independent random variables to the distribution of a similar vector of mutually independent random variables. We examine the...

The fundamental best-possible bounds inequality for bivariate distribution functions with given margins is the Frechet-Hoeffding inequality: If H denotes the joint distribution function of random variables X and Y whose margins are F and G, respectively, then...

We show that Spearman's rho is a measure of average positive (and negative) quadrant dependence, and that Kendall's tau is a measure of average total positivity (and reverse regularity) of order two.

If X and Y are continuous random variables with joint distribution function H, then the Kendall distribution function of (X,Y) is the distribution function of the random variable H(X,Y). Kendall distribution functions arise in the study of stochastic orderings of random vectors. In this paper we...

The copula for a bivariate distribution functionH(x, y) with marginal distribution functionsF(x) andG(y) is the functionCdefined byH(x, y)=C(F(x), G(y)).Cis called Archimedean ifC(u, v)=[phi]-1([phi](u)+[phi](v)), where[phi]is a convex decreasing continuous function on (0, 1]...