Search results for " second-order statistics"

showing 3 items of 3 documents

An Analysis of Earthquakes Clustering Based on a Second-Order Diagnostic Approach

2009

A diagnostic method for space–time point process is here introduced and applied to seismic data of a fixed area of Japan. Nonparametric methods are used to estimate the intensity function of a particular space–time point process and on the basis of the proposed diagnostic method, second-order features of data are analyzed: this approach seems to be useful to interpret space–time variations of the observed seismic activity and to focus on its clustering features.

Diagnostic methodsBasis (linear algebra)Computer scienceNonparametric statisticscomputer.software_genreResidualIntensity functionPoint processPhysics::GeophysicsResidual analysis second-order statistics point process ETAS modelData miningSettore SECS-S/01 - StatisticaFocus (optics)Cluster analysiscomputer
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Diagnostics for nonparametric estimation in space-time seismic processes

2010

In this paper we propose a nonparametric method, based on locally variable bandwidths kernel estimators, to describe the space-time variation of seismic activity of a region of Southern California. The flexible estimation approach is introduced together with a diagnostic method for space-time point process, based on the interpretation of some second-order statistics, to analyze the dependence structure of observed data and suggest directions for fit improvement. In this paper we review a diagnostic method for space-time point processes based on the interpretation of the transformed version of some second-order statistics. The method is useful to analyze dependence structures of observed dat…

Point process second-order statistics residual analysis kernel estimator seismic process.Settore SECS-S/01 - Statistica
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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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