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Abstract Spatial extreme value analysis has been an area of rapid growth in the last decade. The focus has been on modelling the spatial componentwise maxima by max-stable processes. Here, we will explain the limitations of these modelling approaches and show how spatial models can be developed that overcome these deficiencies by exploiting the flexible conditional multivariate extremes models of Heffernan and Tawn (2004). We illustrate the benefits of these new spatial models through applications to North Sea wave analysis and to widespread UK river flood risk analysis. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we use a small amount of video files to design a video-on-demand server. We use the available video information to overcome uncertainties such as future user preference, type of video file (movie, cartoon, documentary), video compression technique, etc. Using principal component analysis we overcome such uncertainties by reducing the dimensionality of the video data, creating a new video trace that captures statistical characteristics of most video files; we call this the characteristic video trace. Using the Pareto probability distribution for the size of the video frames (of the characteristic video trace) and self-similarity we develop a non-asymptotic model which predicts memory buffer size for a required quality of service. By obtaining the necessary parameters for the mathematical model from the characteristic video trace we could design the server without more information. [Copyright &y& Elsevier]