Search results for " kernel estimator"
showing 5 items of 5 documents
Probabilistic forecast for Northern New Zealand seismic process: a kernel-based approach
2009
Forecast of earthquakes of a given area of Northern New Zealand is provided. It is based on the assumption that future earthquakes activity may be based on the smoothing of past earthquakes. Therefore, seismic activity is described by an intensity function factorized into kernel functions which depend on time longitude and latitude of events.
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…
Gamma Kernel Intensity Estimation in Temporal Point Processes
2011
In this article, we propose a nonparametric approach for estimating the intensity function of temporal point processes based on kernel estimators. In particular, we use asymmetric kernel estimators characterized by the gamma distribution, in order to describe features of observed point patterns adequately. Some characteristics of these estimators are analyzed and discussed both through simulated results and applications to real data from different seismic catalogs.
Semi-parametric estimation of conditional intensity functions in inhomogeneous space-time point processes
2009
Dealing with data coming from a space-time inhomogeneous process, there is often the need of obtaining estimates of the conditional intensity function, without a complete defi nition of a parametric model and so nonparametric estimation is required: isotropic or anisotropic kernel estimates can be used. The properties of the intensities estimated are not always good, expecially in seismological field. We could try to choose the bandwidth in order to have good predictive properties of the estimated intensity function. Since a direct ML approach can not be followed, we use an estimation procedure based on the further increments of likelihood obtained adding a new observation. Similarly to cro…