etasFLP
R package etasFLP. Mixed FLP and ML Estimation of ETAS Space-Time Point Processes. Estimation of the components of an ETAS model for earthquake description. Non-parametric background seismicity can be estimated through FLP (Forward Likelihood Predictive), while parametric components are estimated through maximum likelihood. The two estimation steps are alternated until convergence is obtained. For each event the probability of being a background event is estimated and used as a weight for declustering steps. Many options to control the estimation process are present, together with some diagnostic tools. Some descriptive functions for earthquakes catalogs are present; also plot, print, summary, profile methods are defined for main output (objects of class ’etasclass’).
Keywords for this software
References in zbMATH (referenced in 7 articles , 2 standard articles )
Showing results 1 to 7 of 7.
Sorted by year (- Molkenthin, Christian; Donner, Christian; Reich, Sebastian; Zöller, Gert; Hainzl, Sebastian; Holschneider, Matthias; Opper, Manfred: GP-ETAS: semiparametric Bayesian inference for the spatio-temporal epidemic type aftershock sequence model (2022)
- Adelfio, Giada; Agosto, Arianna; Chiodi, Marcello; Giudici, Paolo: Financial contagion through space-time point processes (2021)
- Adelfio, Giada; Chiodi, Marcello: Including covariates in a space-time point process with application to seismicity (2021)
- Lee, Clement; Wilkinson, Darren J.: A hierarchical model of nonhomogeneous Poisson processes for Twitter retweets (2020)
- Li, Chenlong; Song, Zhanjie; Wang, Wenjun: Space-time inhomogeneous background intensity estimators for semi-parametric space-time self-exciting point process models (2020)
- Abdollah Jalilian: ETAS: An R Package for Fitting the Space-Time ETAS Model to Earthquake Data (2019) not zbMATH
- Marcello Chiodi and Giada Adelfio: Mixed Non-Parametric and Parametric Estimation Techniques in R Package etasFLP for Earthquakes’ Description (2017) not zbMATH