This paper discusses the application of three different nonparametric methods for decomposing images into regions which exhibit special stochashc es, together with the statistics in connection With steps in the emplrical estimated distribution functions; 2) detection of stochastic informahon wi image by hypothesis testing; 3) rank order stahstics to ose the different types of stochastic mthin a...

This paper discusses the application of three different nonparametric methods for decomposing images into regions which exhibit special stochashc es, together with the statistics in connection With steps in the emplrical estimated distribution functions; 2) detection of stochastic informahon wi image by hypothesis testing; 3) rank order stahstics to ose the different types of stochastic mthin a...

This paper discusses the application of three different nonparametric methods for decomposing images into regions which exhibit special stochashc es, together with the statistics in connection With steps in the emplrical estimated distribution functions; 2) detection of stochastic informahon wi image by hypothesis testing; 3) rank order stahstics to ose the different types of stochastic mthin a...

This paper discusses the application of three different nonparametric methods for decomposing images into regions which exhibit special stochashc es, together with the statistics in connection With steps in the emplrical estimated distribution functions; 2) detection of stochastic informahon wi image by hypothesis testing; 3) rank order stahstics to ose the different types of stochastic mthin a...

This paper discusses the application of three different nonparametric methods for decomposing images into regions which exhibit special stochashc es, together with the statistics in connection With steps in the emplrical estimated distribution functions; 2) detection of stochastic informahon wi image by hypothesis testing; 3) rank order stahstics to ose the different types of stochastic mthin a...

The purpose of this note is to make available a reasonably complete and straightforward proof of Strassen’s theorem on stochastic domination, and to draw attention to the original paper. We also point out that the maximal possible value of P(Z = Z ′) is actually not reduced by the requirement Z Z ′. Here, Z,Z ′ are stochastic elements that Strassen’s theorem states exist under a stochastic domi...

We introduce a stochastic dynamic optimization problem, where risk aversion is expressed by a stochastic ordering constraint. The constraint requires that a random reward sequence depending on our decisions dominates a given benchmark random sequence. The dominance is defined by discounting both processes with a family of discount sequences, and by applying a univariate order. We describe the g...

A new notion of stochastic ordering is introduced to compare multivariate stochastic risk models with respect to extreme portfolio losses. In the framework of multivariate regular variation comparison criteria are derived in terms of ordering conditions on the spectral measures, which allows for analytical or numerical verification in practical applications. Additional comparison criteria in te...

This paper discusses the application of three different nonparametric methods for decomposing images into regions which exhibit special stochashc es, together with the statistics in connection With steps in the emplrical estimated distribution functions; 2) detection of stochastic informahon wi image by hypothesis testing; 3) rank order stahstics to ose the different types of stochastic mthin a...

This paper discusses the application of three different nonparametric methods for decomposing images into regions which exhibit special stochashc es, together with the statistics in connection With steps in the emplrical estimated distribution functions; 2) detection of stochastic informahon wi image by hypothesis testing; 3) rank order stahstics to ose the different types of stochastic mthin a...