1,721,077 research outputs found
Advanced spatio-temporal point processes for the Sicily seismicity analysis
Full text linkDue to the complexity of the generator process of seismic events, we
study under several aspects the interaction structure between earthquake events using
recently developed spatio-temporal statistical techniques and models. Using
these advanced statistical tools, we aim to characterise the global and local scale
cluster behaviour of the Easter Sicily seismicity considering the catalogue data since
2006, when the Italian National Seismic Network was upgraded and earthquake location
was sensibly improved. Firstly, we characterise the global complex spatiotemporal
interaction structure with the space-time ETAS model where background
seismicity is estimated non-parametrically, while triggered seismicity is estimated
by MLE. After identifying seismic sequences by a clustering technique, we characterise
their spatial and spatio-temporal interaction structures using other advanced
point process models. For the characterisation of the spatial interactions, a version
of hybrid of Gibbs point process models is proposed as method to describe the
multiscale interaction structure of several seismic sequences accounting for both
the attractive and repulsive nature of data. Furthermore, we consider log-Gaussian
Cox processes (LGCP), that are relatively tractable class of empirical models for
describing spatio-temporal correlated phenomena. Several parametric formulation
of spatio-temporal LGCP are estimated, by the minimum contrast procedure, assuming
both separable and non-separable parametric specification of the correlation
function of the underlying Gaussian Random Field
Including covariates in a space-time point process with application to seismicity
Full text linkThe paper proposes a stochastic process that improves the assessment of
events in space and time, considering a contagion model (branching process) within
a regression-like framework to take covariates into account. The proposed approach
develops the Forward Likelihood for prediction (FLP) method for estimating the ETAS
model, including covariates in the model specification of the epidemic component. A
simulation study is carried out for analysing the misspecification model effect under
several scenarios. Also an application to the Italian seismic catalogue is reported,
together with the reference to the developed R packag
Special issue on modelling complex environmental data
No full textThis Collection includes a selection of papers presented at GRASPA 2023, the biennial conference of the Italian Research Group for Environmental Statistics GRASPA-SIS (Section of the Italian Statistical Society) and a European regional conference of the International Environmetrics Society (TIES).
The conference endorses co-operation among statisticians, academics as well as practitioners from government and environmental agencies, promoting the development and the use of statistical methods in environmental sciences.
Emphasis has been given to the statistical modelling of complex environmental data in space and time, motivated by contemporaneous environmental problems including but not limited to pollution, extreme events, epidemiological issues
A PCA-based clustering algorithm for the identification of stratiform and convective precipitation at the event scale: an application to the sub-hourly precipitation of Sicily, Italy
Full text linkUnderstanding the structure of precipitation and its separation into stratiform and convective components is still today one of the important and interesting challenges for the scientific community. Despite this interest and the advances made in this field, the classification of rainfall into convective and stratiform components is still today not trivial. This study applies a novel criterion based on a clustering approach to analyze a high temporal resolution precipitation dataset collected for the period 2002–2018 over the Sicily (Italy). Starting from the rainfall events obtained from this dataset, the developed methodology makes it possible to classify the rainfall events into four different classes, which can be related to the convective and stratiform components of the events on the basis of their hyetograph shapes and average intensities. The results show that the occurrence of stratiform events is always much higher than the convective ones, especially in the winter and spring seasons, while from the summer to the mid-autumn the rainfall depth due to convective events results to be higher than that due to the stratiform events. Moreover, the comparison with a more widely accepted separation methodology demonstrates the physical consistency of the proposed methodology
Financial contagion through space-time point processes
Full text linkWe propose to study the dynamics of financial contagion by means of a class of
point process models employed in the modeling of seismic contagion. The proposal
extends network models, recently introduced to model financial contagion, in a
space-time point process perspective. The extension helps to improve the assessment of credit risk of an institution, taking into account contagion spillover effects
Functional Principal Components direction to cluster earthquake
No full textLooking for curves similarity could be a complex issue characterized by subjective choices related to continuous
transformations of observed discrete data (Chiodi, 1989).
In this paper we combine the aim of finding clusters from a set of individual curves to the functional nature of data,
applying a variant of a k-means algorithm based on the principal component rotation of data. We apply a classical
clustering method to rotated data, according to the direction of maximum variance.
A k-means clustering algorithm based on PCA rotation of data is proposed, as an alternative to methods that
require previous interpolation of data based on splines or linear fitting (García-Escudero and Gordaliza (2005),
Tarpey (2007), Sangalli et al. (2008)).PublishedVienna (Austria)ope
Clustering of Waveforms Based on FPCA Direction
No full textAbstract. Looking for curves similarity could be a complex issue characterized by
subjective choices related to continuous transformations of observed discrete data
(Chiodi, 1989). Waveforms correlation techniques have been introduced to charac-
terize the degree of seismic event similarity (Menke, 1999) and in facilitating more
accurate relative locations within similar event clusters by providing more precise
timing of seismic wave (P and S) arrivals (Phillips, 1997).
In this paper functional analysis (Ramsey, and Silverman, 2006) is considered to
highlight common characteristics of waveforms-data and to summarize these charac-
teristics by few components, by applying a variant of a classical clustering method to
rotated data (Sangalli et al., 2010) according to the direction of maximum variance
(i.e. based on PCA rotation of data).PublishedKarlsruhe (Germany)ope
Clustering of waveforms based on FPCA direction
No full textLooking for curves similarity could be a complex issue characterized by
subjective choices related to continuous transformations of observed discrete data
(Chiodi, 1989). Waveforms correlation techniques have been introduced to charac-
terize the degree of seismic event similarity (Menke, 1999) and in facilitating more
accurate relative locations within similar event clusters by providing more precise
timing of seismic wave (P and S) arrivals (Phillips, 1997).
In this paper functional analysis (Ramsey, and Silverman, 2006) is considered to
highlight common characteristics of waveforms-data and to summarize these charac-
teristics by few components, by applying a variant of a classical clustering method to
rotated data (Sangalli et al., 2010) according to the direction of maximum variance
(i.e. based on PCA rotation of data)
Functional Principal Components direction to cluster earthquake
No full textLooking for curves similarity could be a complex issue characterized by subjective choices related to continuous
transformations of observed discrete data (Chiodi, 1989).
In this paper we combine the aim of finding clusters from a set of individual curves to the functional nature of data,
applying a variant of a k-means algorithm based on the principal component rotation of data. We apply a classical
clustering method to rotated data, according to the direction of maximum variance.
A k-means clustering algorithm based on PCA rotation of data is proposed, as an alternative to methods that
require previous interpolation of data based on splines or linear fitting (García-Escudero and Gordaliza (2005),
Tarpey (2007), Sangalli et al. (2008)).PublishedVienna (Austria)ope
Exploring the effects of temperature on demersal fish communities in the Central Mediterranean Sea using INLA-SPDE modeling approach
Full text linkClimate change significantly impacts marine ecosystems worldwide, leading to alterations in the composition and structure of marine communities. In this study, we aim to explore the effects of temperature on demersal fish communities in the Central Mediterranean Sea, using data collected from a standardized monitoring program over 23 years. Computationally efficient Bayesian inference is performed using the integrated nested Laplace approximation and the stochastic partial differential equation approach to model the spatial and temporal dynamics of the fish communities. We focused on the mean temperature of the catch (MTC) as an indicator of the response of fish communities to changes in temperature. Our results showed that MTC decreased significantly with increasing depth, indicating that deeper fish communities may be composed of colder affinity species, more vulnerable to future warming. We also found that MTC had a step-wise rather than linear increase with increasing water temperature, suggesting that fish communities may be able to adapt to gradual changes in temperature up to a certain threshold before undergoing abrupt changes. Our findings highlight the importance of considering the non-linear dynamics of fish communities when assessing the impacts of temperature on marine ecosystems and provide important insights into the potential impacts of climate change on demersal fish communities in the Central Mediterranean Sea
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