Search results for "Regular variation"

showing 2 items of 2 documents

Large deviations results for subexponential tails, with applications to insurance risk

1996

AbstractConsider a random walk or Lévy process {St} and let τ(u) = inf {t⩾0 : St > u}, P(u)(·) = P(· | τ(u) < ∞). Assuming that the upwards jumps are heavy-tailed, say subexponential (e.g. Pareto, Weibull or lognormal), the asymptotic form of the P(u)-distribution of the process {St} up to time τ(u) is described as u → ∞. Essentially, the results confirm the folklore that level crossing occurs as result of one big jump. Particular sharp conclusions are obtained for downwards skip-free processes like the classical compound Poisson insurance risk process where the formulation is in terms of total variation convergence. The ideas of the proof involve excursions and path decompositions for Mark…

Statistics and ProbabilityExponential distributionRegular variationRuin probabilityExcursionRandom walkDownwards skip-free processLévy processConditioned limit theoremTotal variation convergenceCombinatoricsInsurance riskPath decompositionIntegrated tailProbability theoryModelling and SimulationExtreme value theoryMaximum domain of attractionMathematicsStochastic processApplied MathematicsExtreme value theoryRandom walkSubexponential distributionModeling and SimulationLog-normal distributionLarge deviations theory60K1060F10Stochastic Processes and their Applications
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Hard-Core Thinnings of Germ‒Grain Models with Power-Law Grain Sizes

2013

Random sets with long-range dependence can be generated using a Boolean model with power-law grain sizes. We study thinnings of such Boolean models which have the hard-core property that no grains overlap in the resulting germ‒grain model. A fundamental question is whether long-range dependence is preserved under such thinnings. To answer this question, we study four natural thinnings of a Poisson germ‒grain model where the grains are spheres with a regularly varying size distribution. We show that a thinning which favors large grains preserves the slow correlation decay of the original model, whereas a thinning which favors small grains does not. Our most interesting finding concerns the c…

Statistics and ProbabilityRegular variationDisjoint sets02 engineering and technologyPoisson distribution60D05 60G55Power law01 natural sciencesmarked Poisson processsymbols.namesake010104 statistics & probabilityFOS: Mathematics0202 electrical engineering electronic engineering information engineeringgerm‒grain modelGermStatistical physics60D050101 mathematicsMathematicsta115ta114ThinningBoolean modelApplied MathematicsProbability (math.PR)ta111Boolean model020206 networking & telecommunicationsHard sphereshard-core modelsymbolsSPHERES60G55hard-sphere modelMathematics - ProbabilityAdvances in Applied Probability
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