Alternated estimation in semi-parametric space-time branching-type point processes with application to seismic catalogs

Adelfio, Giada; Chiodi, Marcello

doi:10.1007/s00477-014-0873-8

Cited by 30 publications

(24 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where k 1 = n/2 . Adelfio and Chiodi (2015a) and Adelfio and Chiodi (2015b) developed the FLP method into a semiparametric method following an alternated estimation procedure similar to Algorithm 1. The procedure splits the model parameters into the nonparametric smoothing parameters Σ and the triggering function parameters Θ, and iteratively fits them in the following steps:…”

Section: Forwardmentioning

confidence: 99%

A Review of Self-Exciting Spatio-Temporal Point Processes and Their Applications

Reinhart¹

2018

Statist. Sci.

124

125

View full text Add to dashboard Cite

Self-exciting spatio-temporal point process models predict the rate of events as a function of space, time, and the previous history of events. These models naturally capture triggering and clustering behavior, and have been widely used in fields where spatio-temporal clustering of events is observed, such as earthquake modeling, infectious disease, and crime. In the past several decades, advances have been made in estimation, inference, simulation, and diagnostic tools for self-exciting point process models. In this review, I describe the basic theory, survey related estimation and inference techniques from each field, highlight several key applications, and suggest directions for future research.

show abstract

Section: Forwardmentioning

confidence: 99%

A Review of Self-Exciting Spatio-Temporal Point Processes and Their Applications

Reinhart¹

2018

Statist. Sci.

124

125

View full text Add to dashboard Cite

show abstract

“…The etasFLP package is mainly based on those ideas. In Chiodi (2013, 2015a), we classified events according to their probability of being a background or an offspring event, as proposed by Zhuang et al (2002), and then estimated the space-time intensity of the generating point process of the different components by mixing non-parametric and parametric approaches, applying a forward predictive likelihood estimation approach to semi-parametric models (Chiodi and Adelfio 2011;Adelfio and Chiodi 2015a). The probabilities of being a background event are used as weights in non-parametric intensity estimation of the background seismicity.…”

Section: Introductionmentioning

confidence: 99%

Mixed Non-Parametric and Parametric Estimation Techniques in R Package etasFLP for Earthquakes' Description

Chiodi¹,

Adelfio²

2017

J. Stat. Soft.

Self Cite

View full text Add to dashboard Cite

AbstractetasFLP is an R package which fits an epidemic type aftershock sequence (ETAS) model to an earthquake catalog; non-parametric background seismicity can be estimated through a forward predictive likelihood approach, while parametric components of triggered seismicity are estimated through maximum likelihood; estimation steps are alternated until convergence is obtained and for each event the probability of being a background event is estimated. The package includes options which allow its wide use. Methods for plot, summary and profile are defined for the main output class object. The paper provides examples of the package's use with description of the underlying R and Fortran routines.

show abstract

“…We fitted the model using the R package etasFLP (Chiodi & Adelfio, ) based on the method developed in Chiodi and Adelfio () and Adelfio and Chiodi (). For each event, the probability of being a background event is estimated in order to provide an estimate of weights for a nonparametric density estimation of the spatial background component.…”

Section: Application To Earthquake Datamentioning

confidence: 99%

Testing for local structure in spatiotemporal point pattern data

et al. 2017

Self Cite

View full text Add to dashboard Cite

The detection of clustering structure in a point pattern is one of the main focuses of attention in spatiotemporal data mining. Indeed, statistical tools for clustering detection and identification of individual events belonging to clusters are welcome in epidemiology and seismology. Local second-order characteristics provide information on how an event relates to nearby events. In this work, we extend local indicators of spatial association (known as LISA functions) to the spatiotemporal context (which will be then called LISTA functions). These functions are then used to build local tests of clustering to analyse differences in local spatiotemporal structures. We present a simulation study to assess the performance of the testing procedure, and we apply this methodology to earthquake data

show abstract

Alternated estimation in semi-parametric space-time branching-type point processes with application to seismic catalogs

Cited by 30 publications

References 28 publications

A Review of Self-Exciting Spatio-Temporal Point Processes and Their Applications

A Review of Self-Exciting Spatio-Temporal Point Processes and Their Applications

Mixed Non-Parametric and Parametric Estimation Techniques in R Package etasFLP for Earthquakes' Description

Testing for local structure in spatiotemporal point pattern data

Contact Info

Product

Resources

About