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A conditional variance is an indicator of the level of independence between two random variables. We exploit this intuitive relationship and define a measure v which is almost a measure of mutual complete dependence. Unsurprisingly, the measure attains its minimum value for many pairs of non-independent ran- dom variables. Adjusting the measure so as to make it invariant under all Borel measurable injective trans- formations, we obtain a copula-based measure of dependence v* satisfying A. Rényi’s...
This paper proposes a general framework to compare the strength of the dependence in survival models, as time changes, i. e. given remaining lifetimes , to compare the dependence of given , and given , where . More precisely, analytical results will be obtained in the case the survival copula of is either Archimedean or a distorted copula. The case of a frailty based model will also be discussed in details.
In this paper we first use the semiconormed fuzzy integrals in order to extend the definition of the fuzzy expected value (F.E.V.) (Kandel, 1979). We generalize some of the properties due to Kandel with a criticism about his purpose of constraining the F.E.V. to be linear. Finally, a necessary and sufficient condition is given in order to guarantee some linearity properties for any semiconormed fuzzy integral.
Let (X,A) and (Y,B) be measurable spaces. Supposewe are given a probability α on A, a probability β on B and a probability μ on the product σ-field A ⊗ B. Is there a probability ν on A⊗B, with marginals α and β, such that ν ≪ μ or ν ~ μ ? Such a ν, provided it exists, may be useful with regard to equivalent martingale measures and mass transportation. Various conditions for the existence of ν are provided, distinguishing ν ≪ μ from ν ~ μ.
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