Search results for " conditional intensity function"

showing 4 items of 4 documents

An algorithm for earthquakes clustering based on maximum likelihood

2007

In this paper we propose a clustering technique set up to separate and find out the two main components of seismicity: the background seismicity and the triggered one. We suppose that a seismic catalogue is the realization of a non homogeneous space-time Poisson clustered process, with a different parametrization for the intensity function of the Poisson-type component and of the clustered (triggered) component. The method here proposed assigns each earthquake to the cluster of earthquakes, or to the set of independent events, according to the increment to the likelihood function, computed using the conditional intensity function estimated by maximum likelihood methods and iteratively chang…

business.industryPattern recognitionMaximum likelihood sequence estimationPoisson distributionPoint processPhysics::Geophysicssymbols.namesakeCURE data clustering algorithmsymbolsETAS model earthquakes point process clusteringArtificial intelligenceSettore SECS-S/01 - Statisticaclustering earthquakesCluster analysisLikelihood functionbusinessAlgorithmPoint processes conditional intensity function likelihood function clustering methodRealization (probability)k-medians clusteringMathematics
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Point process diagnostics based on weighted second-order statistics and their asymptotic properties

2008

A new approach for point process diagnostics is presented. The method is based on extending second-order statistics for point processes by weighting each point by the inverse of the conditional intensity function at the point’s location. The result is generalized versions of the spectral density, R/S statistic, correlation integral and K-function, which can be used to test the fit of a complex point process model with an arbitrary conditional intensity function, rather than a stationary Poisson model. Asymptotic properties of these generalized second-order statistics are derived, using an approach based on martingale theory.

Statistics and ProbabilityMathematical optimizationSpectral densityInverseResidual analysis point process second-order analysis conditional intensity functionResidualPoint processWeightingCorrelation integralApplied mathematicsPoint (geometry)Settore SECS-S/01 - StatisticaStatisticMathematicsAnnals of the Institute of Statistical Mathematics
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Comparison between nonparametric and parametric estimate of the conditional intensity function of a seismic space-time point process

2008

A seismic gap can be defined as a segment of an active geologic fault that has not produced seismic events for an unusually long time; gaps are often considered susceptible to future strong earthquakes occurrence and therefore their identification may be useful for predictive purposes. In this paper we try to identify gaps in an area of South Tyrrhenian Sea by comparing the observed seismicity, estimated by nonparametric method, and the theoretical one, described by a particular space-time point process (ETAS model).

Settore SECS-S/01 - Statisticapoint processes conditional intensity function kernel estimate ETAS model seismic gaps
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Space-Time Forecasting of Seismic Events in Chile

2017

The aim of this work is to study the seismicity in Chile using the ETAS (epidemic type aftershock sequences) space‐time approach. The proposed ETAS model is estimated using a semi‐parametric technique taking into account the parametric and nonparametric components corresponding to the triggered and background seismicity, respectively. The model is then used to predict the temporal and spatial intensity of events for some areas of Chile where recent large earthquakes (with magnitude greater than 8.0 M) occurred.

space‐time point processes conditional intensity function ETAS model etasFLP(R package) forecastSpace timeforecsting Chile esrthquakesSettore SECS-S/01 - StatisticaGeologySeismology
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