Joint second-order parameter estimation for spatio-temporal log-Gaussian Cox processes

Siino, Marianna; Adelfio, Giada; Mateu, Jorge

doi:10.1007/s00477-018-1579-0

Cited by 16 publications

(27 citation statements)

References 44 publications

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“…LGCP is largely used for analysing spatial point pattern for earthquake data (Møller et al, 1998;Siino et al, 2018).…”

Section: Continuous Domain Models For Spatial Point Processesmentioning

confidence: 99%

“…In the first step, a Poisson model with the same model formula is fitted to the point pattern data, providing the estimates of the coefficients of all the terms in the model formula that characterize the intensity. Then, in the second step, the estimated intensity is taken as the true one and the cluster or correlation parameters are estimated by the method of minimum contrast (Pfanzagl, 1969;Eguchi, 1983;Diggle, 1979;Diggle and Gratton, 1984;Siino et al, 2018), or the Palm likelihood (Ogata and Katsura, 1991;Tanaka et al, 2008) or composite likelihood (Guan, 2006).…”

Section: Continuous Domain Models For Spatial Point Processesmentioning

confidence: 99%

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Spatial Bayesian Hierarchical Modelling with Integrated Nested Laplace Approximation

D'Angelo,

Abbruzzo,

Adelfio

2020

Preprint

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We consider latent Gaussian fields for modelling spatial dependence in the context of both spatial point patterns and areal data, providing two different applications. The inhomogeneous Log-Gaussian Cox Process model is specified to describe a seismic sequence occurred in Greece, resorting to the Stochastic Partial Differential Equations. The Besag-York-Molli model is fitted for disease mapping of the Covid-19 infection in the North of Italy. These models both belong to the class of Bayesian hierarchical models with latent Gaussian fields whose posterior is not available in closed form. Therefore, the inference is performed with the Integrated Nested Laplace Approximation, which provides accurate and relatively fast analytical approximations to the posterior quantities of interest.

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“…LGCP is largely used for analysing spatial point pattern for earthquake data (Møller et al, 1998;Siino et al, 2018).…”

Section: Continuous Domain Models For Spatial Point Processesmentioning

confidence: 99%

Section: Continuous Domain Models For Spatial Point Processesmentioning

confidence: 99%

Spatial Bayesian Hierarchical Modelling with Integrated Nested Laplace Approximation

D'Angelo,

Abbruzzo,

Adelfio

2020

Preprint

Self Cite

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“…Statistical methodology based on point processes has become one of the standard tools for the modeling of earthquake occurrences, e.g. Vere-Jones (1970); Ogata (1988Ogata ( , 1999; Matsu'ura and Karakama (2005); Siino et al (2018) and references therein. To analyze such data, the pattern of earthquake locations (resp.…”

Section: Introductionmentioning

confidence: 99%

“…Ogata, 1999;Anwar et al, 2012) while intensity-based modeling includes Poisson and Cox point processes (e.g. Türkyilmaz et al, 2013;Siino et al, 2018;Aisah et al, 2020). The model using conditional intensity or intensity is complementary and each has its virtues, see e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Cox models have been considered and are standard for earthquake modeling, especially the Thomas process and LGCP (e.g. Vere-Jones, 1970;Türkyilmaz et al, 2013;Siino et al, 2018). Nevertheless, no study considering Cox models include geological variables in the analysis, thus extending these models to be able to capture the effect of geological factors is a major importance.…”

Section: Introductionmentioning

confidence: 99%

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Quantifying effect of geological factor on distribution of earthquake occurrences by inhomogeneous Cox processes

Choiruddin,

Trisnisa,

Iriawan

2021

Preprint

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Research on the earthquake occurrences using a statistical methodology based on point processes has mainly focused on the data featuring earthquake catalogs which ignores the effect of environmental variables. In this paper, we introduce inhomogeneous versions of the Cox process models which are able to quantify the effect of geological factors such as subduction zone, fault, and volcano on the major earthquake distribution in Sulawesi and Maluku, Indonesia. In particular, we compare Thomas, Cauchy, variance-Gamma, and log-Gaussian Cox models and consider parametric intensity and pair correlation functions to enhance model interpretability. We perform model selection using the Akaike information criterion (AIC) and envelopes test. We conclude that the nearest distances to the subduction zone and volcano give a significant impact on the risk of earthquake occurrence in Sulawesi and Maluku. Furthermore, the Cauchy and variance-Gamma cluster models fit well the major earthquake distribution in Sulawesi and Maluku.

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Quantifying the Effect of Geological Factors on Distribution of Earthquake Occurrences by Inhomogeneous Cox Processes

Choiruddin

Aisah

Trisnisa

et al. 2021

Pure Appl. Geophys.

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Joint second-order parameter estimation for spatio-temporal log-Gaussian Cox processes

Cited by 16 publications

References 44 publications

Spatial Bayesian Hierarchical Modelling with Integrated Nested Laplace Approximation

Spatial Bayesian Hierarchical Modelling with Integrated Nested Laplace Approximation

Quantifying effect of geological factor on distribution of earthquake occurrences by inhomogeneous Cox processes

Quantifying the Effect of Geological Factors on Distribution of Earthquake Occurrences by Inhomogeneous Cox Processes

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