170,962 research outputs found
Aging functions and multivariate notions of NBU and IFR
No full textFor d≥2, let X=(X1, …, Xd) be a vector of exchangeable continuous lifetimes with joint survival function . For such models, we study some properties of multivariate aging of that are described by means of the multivariate aging function , which is a useful tool for describing the level curves of . Specifically, the attention is devoted to notions that generalize the univariate concepts of New Better than Used and Increasing Failure Rate. These multivariate notions are satisfied by random vectors whose components are conditionally independent and identically distributed having univariate conditional survival function that is New Better than Used (respectively, Increasing Failure Rate). Furthermore, they also have an interpretation in terms of comparisons among conditional survival functions of residual lifetimes, given a same history of observed survivals
Jan-Frederik Mai, Matthias Scherer, Simulating copulas: Stochastic models, sampling algorithms and applications, London, Imperial College Press, 2012by J.-F. Mai and M. Scherer”
No full textComponentwise concave copulas and their asymmetry
Get PDFsummary:The class of componentwise concave copulas is considered, with particular emphasis on its closure under some constructions of copulas (e.g., ordinal sum) and its relations with other classes of copulas characterized by some notions of concavity and/or convexity. Then, a sharp upper bound is given for the -measure of non-exchangeability for copulas belonging to this class
Evolution of the Dependence of Residual Lifetimes
Get PDFWe investigate the dependence properties of a vector of residual lifetimes by means of the copula associated with the conditional distribution function. In particular, the evolution of positive dependence properties (like quadrant dependence and total positivity) are analyzed and expressions for the evolution of measures of association are given
Copulas, Tail Dependence and Applications to the Analysis of Financial Time Series
No full textTail dependence is an important property of a joint distribution function that has a huge impact on the determination of risky quantities associated to a stochastic model (Value-at-Risk, for instance). Here we aim at presenting some investigations about tail dependence including the following aspects: the determination of suitable stochastic models to be used in extreme scenarios; the notion of threshold copula, that helps in describing the tail of a joint distribution. Possible applications of the introduced concepts to the analysis of financial time series are presented with particular emphasis on cluster methods and determination of possible contagion effects among markets
Construction of non-exchangeable bivariate distribution functions
No full textBivariate distribution function, Copula, Exchangeability,
Direction Dependence in Statistical Modeling: Methods of AnalysisWolfgangWiedermann, DaeyoungKim, Engin A.Sungur and AlexandervonEyeWiley, 2021, 432 pages, £102, hardcover ISBN: 978‐1‐119‐52307‐9
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