Search results for "Poisson point process"

showing 4 items of 4 documents

A semi-parametric stochastic generator for bivariate extreme events

2017

The analysis of multiple extreme values aims to describe the stochastic behaviour of observations in the joint upper tail of a distribution function. For instance, being able to simulate multivariate extreme events is convenient for end users who need a large number of random replications of extremes as input of a given complex system to test its sensitivity. The simulation of multivariate extremes is often based on the assumption that the dependence structure, the so-called extremal dependence function, is described by a specific parametric model. We propose a simulation method for sampling bivariate extremes, under the assumption that the extremal dependence function is semiparametric. Th…

Bivariate extreme-value distributionAngular measurePickands dependence functionBernstein polynomialsGeneralized extreme-value distributionSettore SECS-S/01 - StatisticaExtremal dependencePoisson point processWind speed
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The Homogeneous Poisson Point Process

2008

symbols.namesakeComplete spatial randomnessUniqueness theorem for Poisson's equationCompound Poisson processMathematical analysisDiscrete Poisson equationHomogeneous poisson point processsymbolsFractional Poisson processMathematics
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The Poisson Point Process

2020

Poisson point processes can be used as a cornerstone in the construction of very different stochastic objects such as, for example, infinitely divisible distributions, Markov processes with complex dynamics, objects of stochastic geometry and so forth.

symbols.namesakeCompound Poisson distributionComputer sciencePoisson point processCompound Poisson processsymbolsMarkov processStatistical physicsFractional Poisson processLévy processStochastic geometryPoint process
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Boolean Models: Maximum Likelihood Estimation from Circular Clumps

1990

This paper deals with the problem of making inferences on the maximum radius and the intensity of the Poisson point process associated to a Boolean Model of circular primary grains with uniformly distributed random radii. The only sample information used is observed radii of circular clumps (DUPAC, 1980). The behaviour of maximum likelihood estimation has been evaluated by means of Monte Carlo methods.

Statistics and ProbabilityMathematical optimizationEstimation theoryBoolean modelMonte Carlo methodMathematical analysisGeneral MedicineRadiusMaximum likelihood sequence estimationPoisson point processBoolean expressionStatistics Probability and UncertaintyIntensity (heat transfer)MathematicsBiometrical Journal
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