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...

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 most of the results obtained about stochastic properties of generalized order statistics and their spacings in the literature are based on equal model parameters. In this paper, with less restrictive conditions on the model parameters, we prove some new multivariate likelihood ratio ordering results between two sub-vectors of GOS's as well as two sub-vectors of $p$-spacings based on two con...