Search results for " point processes"

showing 9 items of 29 documents

Windowed Etas Models With Application To The Chilean Seismic Catalogs

2015

Abstract The seismicity in Chile is estimated using an ETAS (Epidemic Type Aftershock sequences) space–time point process through a semi-parametric technique to account for the estimation of parametric and nonparametric components simultaneously. The two components account for triggered and background seismicity respectively, and are estimated by alternating a ML estimation for the parametric part and a forward predictive likelihood technique for the nonparametric one. Given the geographic and seismological characteristics of Chile, the sensitivity of the technique with respect to different geographical areas is examined in overlapping successive windows with varying latitude. A different b…

Statistics and ProbabilityNonparametric statisticsManagement Monitoring Policy and LawInduced seismicityGeodesyPoint processPhysics::GeophysicsLatitudeSpace-time point processes ETAS model etasFLP R packagePredictive likelihoodStatisticsSensitivity (control systems)Computers in Earth SciencesAftershockGeologyParametric statistics
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Cluster priors in the Bayesian modelling of fMRI data

2001

bildanalysmarked point processesMonte Carlo -menetelmätMarkov chain Monte Carloimage analysiskuva-analyysiMarkovin ketjutmagneettitutkimusaivotfunctional magnetic resonance imaginghuman brainBayesian modellingMarkovkedjor
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Flexible space-time process for seismic data

2009

Point processes are well studied objects in probability theory and a powerful tool in statistics for modelling and analyzing the distribution of real phenomena, such as the seismic one. Point processes can be specified mathematically in several ways, for instance, by considering the joint distributions of the counts of points in arbitrary sets or defining a complete intensity function. The conditional intensity function is a function of the point history and it is itself a stochastic process depending on the past up to time t. In this paper some techniques to estimate the intensity function of space-time point processes are developed, by following semi-parametric approaches and diagnostic m…

flexible estimation second-order diagnostics point processes earthquakesSettore SECS-S/01 - Statistica
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Measuring the Rate of Information Exchange in Point-Process Data With Application to Cardiovascular Variability

2022

The amount of information exchanged per unit of time between two dynamic processes is an important concept for the analysis of complex systems. Theoretical formulations and data-efficient estimators have been recently introduced for this quantity, known as the mutual information rate (MIR), allowing its continuous-time computation for event-based data sets measured as realizations of coupled point processes. This work presents the implementation of MIR for point process applications in Network Physiology and cardiovascular variability, which typically feature short and noisy experimental time series. We assess the bias of MIR estimated for uncoupled point processes in the frame of surrogate…

information dynamics point processes mutual information rate heart rate variability cardiovascular time seriesmutual information rateSettore ING-INF/06 - Bioingegneria Elettronica E Informaticaheart rate variabilityinformation dynamicscardiovascular time seriespoint processespoint processFrontiers in Network Physiology
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Community detection of seismic point processes

2022

In this paper, we combine robin and Local Indicators of Spatio-Temporal Association (LISTA) functions. robin is an R package to assess the robustness of the community structure of a network found by one or more methods to give indications about their reliability. We use it to propose a classification algorithm of events in a spatio-temporal point pattern, by means of the local second-order characteristics and the community detection procedure in network analysis. We demonstrate the proposed procedure on a real data analysis on seismic data.

network analysis community detection algorithm second-order characteristics spatio-temporal point processes statistical validation earthquakesSettore SECS-S/01 - Statistica
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Nonparametric intensity estimation in space-time point processes and application to seismological problems

2008

space-time point processes intensity function kernel estimator
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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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Models and methods for space and space-time interactions in complex point processes with applications on earthquakes

spatial covariatespatial point processeearthquakes; hybrids of Gibbs point processes; spatial covariates; spatial point processes; hypothesis testing; local indicators of spatio-temporal association; permutation-based tests; second-order product density function; log-Gaussian Cox process; spatial anisotropy; spatio-temporal point process; clustering detectionlog-Gaussian Cox proceearthquakehybrids of Gibbs point processehypothesis testinglocal indicators of spatio-temporal associationpermutation-based testspatial anisotropysecond-order product density functionspatio-temporal point proceSettore SECS-S/01 - Statisticaclustering detection
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Weighted local second-order statistics for complex spatio-temporal point processes

2019

Spatial, temporal, and spatio-temporal point processes, and in particular Poisson processes, are stochastic processes that are largely used to describe and model the distribution of a wealth of real phenomena. When a model is fitted to a set of random points, observed in a given multidimensional space, diagnostic measures are necessary to assess the goodness-of-fit and to evaluate the ability of that model to describe the random point pattern behaviour. The main problem when dealing with residual analysis for point processes is to find a correct definition of residuals. Diagnostics of goodness-of-fit in the theory of point processes are often considered through the transformation of data in…

spatio-temporal point processes diagnostics K-function weighted second-order statistics
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