Geostatistics of extreme values makes it possible to model the asymptotic behavior of random phenomena that depend on time or space. In this paper, we propose new models of the extremal coefficient of a stationary ran...Geostatistics of extreme values makes it possible to model the asymptotic behavior of random phenomena that depend on time or space. In this paper, we propose new models of the extremal coefficient of a stationary random field where the cumulative distribution is associated with a multivariate copula. More precisely, some models of extensions of the extremogram and these derivatives are built in a spatial framework. Moreover, both these two geostatistical tools are modeled using the extremal variogram which characterizes the asymptotic stochastic behavior of the phenomena.展开更多
In this paper we estimate the incubation period of a possible pathology following exposure to dioxins during a poor diet. The tools developed for this purpose include the probabilistic extremal model and the stochasti...In this paper we estimate the incubation period of a possible pathology following exposure to dioxins during a poor diet. The tools developed for this purpose include the probabilistic extremal model and the stochastic behavior of the distribution tails of the contamination. We propose a cumulative distribution function for a random variable that follows both a Gaussian distribution and a GPD. A global optimization method is also explored for the efficient estimation of parameters of GPD.展开更多
In this paper, we present a stochastic model for data in a Wireless Sensor Network (WSN) using random field theory. The model captures the space-time behavior of the underlying phenomenon being observed by the network...In this paper, we present a stochastic model for data in a Wireless Sensor Network (WSN) using random field theory. The model captures the space-time behavior of the underlying phenomenon being observed by the network. We present results regarding the size and spatial distribution of the regions of the network that sense statistically extreme values of the underlying phenomenon using the theory of extreme excursion regions. These results compliment many existing works in the literature that describe algorithms to reduce the data load, but lack an analytical approach to evaluate the size and spatial distribution of this load. We show that if only the statistically extreme data is transmitted in the network, then the data load can be significantly reduced. Finally, a simple performance model of a WSN is developed based on a collection of asynchronous M/M/1 servers that work in parallel. We derive several performance measures from this performance model. The presented results will be useful in the design of large scale sensor networks.展开更多
文摘Geostatistics of extreme values makes it possible to model the asymptotic behavior of random phenomena that depend on time or space. In this paper, we propose new models of the extremal coefficient of a stationary random field where the cumulative distribution is associated with a multivariate copula. More precisely, some models of extensions of the extremogram and these derivatives are built in a spatial framework. Moreover, both these two geostatistical tools are modeled using the extremal variogram which characterizes the asymptotic stochastic behavior of the phenomena.
文摘In this paper we estimate the incubation period of a possible pathology following exposure to dioxins during a poor diet. The tools developed for this purpose include the probabilistic extremal model and the stochastic behavior of the distribution tails of the contamination. We propose a cumulative distribution function for a random variable that follows both a Gaussian distribution and a GPD. A global optimization method is also explored for the efficient estimation of parameters of GPD.
文摘In this paper, we present a stochastic model for data in a Wireless Sensor Network (WSN) using random field theory. The model captures the space-time behavior of the underlying phenomenon being observed by the network. We present results regarding the size and spatial distribution of the regions of the network that sense statistically extreme values of the underlying phenomenon using the theory of extreme excursion regions. These results compliment many existing works in the literature that describe algorithms to reduce the data load, but lack an analytical approach to evaluate the size and spatial distribution of this load. We show that if only the statistically extreme data is transmitted in the network, then the data load can be significantly reduced. Finally, a simple performance model of a WSN is developed based on a collection of asynchronous M/M/1 servers that work in parallel. We derive several performance measures from this performance model. The presented results will be useful in the design of large scale sensor networks.
