How to model multivariate extremes if one must?

How to model multivariate extremes if one must?

In this paper we discuss some approaches to modeling extremely large values in multivariate time series. In particular, we discuss the notion of multivariate regular variation as a key to modeling multivariate heavy-tailed phenomena. The latter notion has found a variety of applications in queuing theory, stochastic networks, telecommunications, insurance, finance and other areas. We contrast t...

How to Model Operational Risk If You Must

Measures of incoherence : How not to gamble if you must

The degree of incoherence, when previsions are not made in accordance with a probability measure, is measured by the rate at which an incoherent bookie can be made a sure loser. We consider each bet from three points of view: that of the gambler, that of the bookie, and a neutral viewpoint. From each viewpoint, we de ne an normalization for each bet, and the sure loss for incoherent previsions ...

How to sample if you must: on optimal functional sampling

We examine a fundamental problem that models various active sampling setups, such as network tomography. We analyze sampling of a multivariate normal distribution with an unknown expectation that needs to be estimated: in our setup it is possible to sample the distribution from a given set of linear functionals, and the difficulty addressed is how to optimally select the combinations to achieve...

How to Count Citations If You Must: Comment

Perry and Reny (2016) show that the Euclidean length of a citation list satisfies five axioms including "depth relevance". We explore "breadth relevance", which favors consistent achievers over one-hit wonders. A convex combination of depth and breadth relevant citation metrics does not satisfy the independence axiom, but violations are rare. We estimate the parameters of this metric using two ...