Multidimensional Clustering and Registration of Seismic Waveform Data

Including covariates in a space-time point process with application to seismicity

AbstractThe paper proposes a spatio-temporal 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 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 package.

Gamma kernel intensity estimation in time point processes

Second-order diagnostics for space-time point processes with application to seismic events

A diagnostic method for space-time point process is introduced and used to interpret and assess the goodness of fit of particular models to real data such as the seismic ones. The proposed method is founded on the definition of a weighted process and allows to detect second-order features of data, like long-range dependence and fractal behavior, that are not accounted for by the fitted model. Applications to earthquake data are provided. Copyright © 2008 John Wiley & Sons, Ltd.

An algorithm for earthquakes clustering based on maximum likelihood

In this paper we propose a clustering technique set up to separate and find out the two main components of seismicity: the background seismicity and the triggered one. We suppose that a seismic catalogue is the realization of a non homogeneous space-time Poisson clustered process, with a different parametrization for the intensity function of the Poisson-type component and of the clustered (triggered) component. The method here proposed assigns each earthquake to the cluster of earthquakes, or to the set of independent events, according to the increment to the likelihood function, computed using the conditional intensity function estimated by maximum likelihood methods and iteratively chang…

Un metodo per l'identificazione di cluster di eventi sismici. 23° Convegno G.N.G.T.S (Roma 2004)

ETAS Space–Time Modeling of Chile Triggered Seismicity Using Covariates: Some Preliminary Results

Chilean seismic activity is one of the strongest in the world. As already shown in previous papers, seismic activity can be usefully described by a space–time branching process, such as the ETAS (Epidemic Type Aftershock Sequences) model, which is a semiparametric model with a large time-scale component for the background seismicity and a small time-scale component for the triggered seismicity. The use of covariates can improve the description of triggered seismicity in the ETAS model, so in this paper, we study the Chilean seismicity separately for the North and South area, using some GPS-related data observed together with ordinary catalog data. Our results show evidence that the use of s…

A penalized approach to covariate selection through quantile regression coefficient models

The coefficients of a quantile regression model are one-to-one functions of the order of the quantile. In standard quantile regression (QR), different quantiles are estimated one at a time. Another possibility is to model the coefficient functions parametrically, an approach that is referred to as quantile regression coefficients modeling (QRCM). Compared with standard QR, the QRCM approach facilitates estimation, inference and interpretation of the results, and generates more efficient estimators. We designed a penalized method that can address the selection of covariates in this particular modelling framework. Unlike standard penalized quantile regression estimators, in which model selec…

GLM-based automatic picking of waveforms

Space-time Point Processes semi-parametric estimation with predictive measure information

In this paper, we provide a method to estimate the space-time intensity of a branching-type point process by mixing nonparametric and parametric approaches. The method accounts simultaneously for the estimation of the different model components, applying a forward predictive likelihood estimation approach to semi-parametric models.

FLP estimation of semi-parametric models for space-time point processes and diagnostic tools

Abstract The conditional intensity function of a space–time branching model is defined by the sum of two main components: the long-run term intensity and short-run term one. Their simultaneous estimation is a complex issue that usually requires the use of hard computational techniques. This paper deals with a new mixed estimation approach for a particular space–time branching model, the Epidemic Type Aftershock Sequence model. This approach uses a simultaneous estimation of the different model components, alternating a parametric step for estimating the induced component by Maximum Likelihood and a non-parametric estimation step, for the background intensity, by FLP (Forward Predictive Like…

Un metodo per l’identificazione di clusters di eventi sismici

The Analysis of Auxological Data by Means of Nonlinear Multivariate Growth Curves

In this paper we treat the problem to analyse a data set constituted by multivariate growth curves for different subjects; thus in this context we deal with 3-way data tables. Nevertheless, it is not possible using factorial techniques proposed to deal with 3-way data matrices, because the observations are generally not equally spaced; moreover a multilevel approach founded on polynomial models is not suitable to deal with intrinsic nonlinear models. We propose a non-factorial technique to analyse auxological data sets using an intrinsic nonlinear multivariate growth model with autocorrelated errors. The application to a real data set of growing children gave easily interpretable results.

Analysis and assessment of trace element contamination in offshore sediments of the Augusta Bay (SE Sicily): A multivariate statistical approach based on canonical correlation analysis and mixture density estimation approach

Abstract An application of multivariate statistical methods is provided to identify anthropogenic contaminants and lithogenic elements in offshore sediments collected near the heavily industrialized Augusta Bay, Sicily. An exploratory statistical technique, based on canonical correlation analysis (CCA) and mixture density estimation approach, is used for distinguishing between natural and anthropogenic contributions of trace elements in the investigated sediments. Following the intensive industrialization of Augusta area, marine sediments reveal the severe impact of local anthropogenic activities for many elements (e.g. As, Cd, Hg, Pb, and Sb), which are considered very dangerous for the en…

How to define deviance residuals in multinomial regression.

This work is devoted to the study of diagnostic tools for categorical data models with an emphasis on the presence of continuous covariates. In particular, the aim is to define a new class of residuals from the parametric multinomial family of models and to study their asymptotics properties. In logistic regression (as in generalized linear models), there are a few different kinds of residuals; we propose a generalization of deviance residuals as defined in logistic regression to the multinomial case and propose their use in order to identify inadequacies in a multinomial model.

Evaluating the performance of a new picking algorithm based on the variance piecewise constant models

In this paper, a new picking algorithm for the automatic seismogram onset time determination is tested on a dataset of simulated waveforms. We aim at capturing the variations in the performance due to some characteristics of both the seismic event and its detection, which in turn affect some characteristics of the waveforms. We therefore simulate seismic events with different magnitude, and assumed to be detected with different distances from the nearest seismic station. Our tests permit to highlight the scenarios most suitable for our algorithm.

Mixed estimation technique in semi-parametric space-time point processes for earthquake description

An estimation approach for the semi-parametric intensity function of a particular space-time point process is introduced. In particular we want to account for the estimation of parametric and nonparametric components simultaneously, applying a forward predictive likelihood to semi-parametric models. For each event, the probability of being a background event or one belonging to a seismic sequence is therefore estimated.

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

etasFLP 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.

Windowed Etas Models With Application To The Chilean Seismic Catalogs

Abstract The seismicity in Chile is estimated using an ETAS (Epidemic Type Aftershock sequences) space–time point process through a semi-parametric technique to account for the estimation of parametric and nonparametric components simultaneously. The two components account for triggered and background seismicity respectively, and are estimated by alternating a ML estimation for the parametric part and a forward predictive likelihood technique for the nonparametric one. Given the geographic and seismological characteristics of Chile, the sensitivity of the technique with respect to different geographical areas is examined in overlapping successive windows with varying latitude. A different b…

Gamma Kernel Intensity Estimation in Temporal Point Processes

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.

Forward likelihood-based predictive approach for space-time point processes

Dealing with data from a space–time point process, the estimation of the conditional intensity function is a crucial issue even if a complete definition of a parametric model is not available. In particular, in case of exploratory contexts or if we want to assess the adequacy of a specific parametric model, some kind of nonparametric estimation procedure could be useful. Often, for these purposes kernel estimators are used and the estimation of the intensity function depends on the estimation of bandwidth parameters. In some fields, like for instance the seismological one, predictive properties of the estimated intensity function are pursued. Since a direct ML approach cannot be used, we pr…

Monitoring of the effect of solar radiation and rain on the building envelope with integrated vertical vegetation

The goal of the present paper is the verification of the improvement of the performance of a building envelope with a green wall also in conditions of high irradiance (≥0.6 kW/m2) and with variable meteorological conditions (sunny, cloudy, and rainy), with a focus on intense rainfall and tempest. The object of the analysis has been the Innovation and Technology for Development Center in the University Campus of the Polytechnics of Madrid, where a modular system of integrated vertical vegetation has been installed on the skin of the South and West prospects. The study is based on the analysis of the effective thermoregulation capacity of the system in different climatic situations and has be…

Diagnostics for nonparametric estimation in space-time seismic processes

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…

Clustering of waveforms-data based on FPCA direction

The necessity of nding similar features of waveforms data recorded for earthquakes at di erent time instants is here considered, since eventual similarity between these functions could suggest similar behavior of the source process of the corresponding earthquakes. In this paper we develop a clustering algorithm for curves based on directions de ned by an application of PCA to functional data.

Statistical Analysis of Macroseismic Data for a better Evaluation of Earthquakes Attenuation Laws

In this work we propose a statistical approach, based on the joint analysis of macroseismic data of Italian seismic events of the last two centuries, with which we obtain simultaneously maximum likelihood estimates of attenuation laws and coordinates of hypocenters. Our first results encourage us to use in the future more complex models, with a larger number of historical earthquakes.

Kernel intensity for space-time point processes with application to seismological problems

Dealing with data coming from a space-time inhomogeneous process, there is often the need of semi-parametric estimates of the conditional intensity function; isotropic or anisotropic multivariate kernel estimates can be used, with windows sizes h. The properties of the intensities estimated with this choice of h are not always good for specific fields of application; we could try to choose h in order to have good predictive properties of the estimated intensity function. Since a direct ML approach cannot be followed, we propose an estimation procedure, computationally intensive, based on the subsequent increments of likelihood obtained adding an observation at time. The first results obtain…

Functional Principal components direction to cluster earthquake waveforms

Looking 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 (Garc´ıa- Escudero and Gordali…

Nonparametric clustering of seismic events

In this paper we propose a clustering technique, based on the maximization of the likelihood function defined from the generalization of a model for seismic activity (ETAS model, (Ogata (1988))), iteratively changing the partitioning of the events. In this context it is useful to apply models requiring the distinction between independent events (i.e. the background seismicity) and strongly correlated ones. This technique develops nonparametric estimation methods of the point process intensity function. To evaluate the goodness of fit of the model, from which the clustering method is implemented, residuals process analysis is used.

Stima semi-parametrica della funzione di intensità di un processo di punto spazio-temporale.

Nonparametric intensity estimation in space-time point processes and application to seismological problems

Functional Data Analysis for Optimizing Strategies of Cash-Flow Management

The cash management deals with problem of automating and managing cash-flow processes. Optimization of the management processes greatly reduces overall cash handling costs. The present analysis is an empirical study of cash flows, from and to bank branches, deriving an underlying theoretical framework, which can in a reasonable way be connected with the optimal strategy. Functional data analysis is considered an appropriate framework to analyze the dynamics of the time series behavior of cash flows: since the observations are not equally spaced in time and their number is different for each series, they are converted into a collection of random curves in a space spanned by finite dimensiona…

FPCA Algorithm For Waveform Clustering

Similar features between waveform data recorded for earthquakes at different time instants could suggest similar behavior of the source process of the corresponding source seismic process. In this paper we combine the aim of finding clusters from a set of individual waveform curves with the functional nature of data, applying a variant of a k-means algorithm based on the principal component rotation of data. This approach overcome the limitation of the cross-correlation, and represents an alternative to methods based on the interpolation of data by splines or linear fitting.

Spatial pattern analysis using hybrid models: an application to the Hellenic seismicity

Earthquakes are one of the most destructive natural disasters and the spatial distribution of their epi- centres generally shows diverse interaction structures at different spatial scales. In this paper, we use a multi-scale point pattern model to describe the main seismicity in the Hellenic area over the last 10 years. We analyze the interaction between events and the relationship with geo- logical information of the study area, using hybrid models as proposed by Baddeley et al. ( 2013 ). In our analysis, we find two competing suitable hybrid models, one with a full parametric structure and the other one based on nonpara- metric kernel estimators for the spatial inhomogeneity.

Relazioni tra sismicita’ e tettonica nel margine settentrionale della Sicilia

An integrated approach to investigate the seismotectonics of northern Sicily and southern Tyrrhenian

Abstract This paper deals with a comparison among recent structure and seismicity in the hinge zone between northern Sicily and southern Tyrrhenian, corresponding to both emerged and submerged northern portion of the Maghrebian chain. This hinge zone is part of a wider W–E trending right-lateral shear zone, mainly characterized by both a synthetic NW-SE/W–E oriented, and antithetic left-lateral N–S/NE-SW fault systems, which has been affecting the tectonic edifice, since the Pliocene. The inland structures have been mapped using aerial-photo interpretation, geological mapping and mesostructural analysis to reconstruct the stress regime in the study area. On the contrary, the offshore struct…

ETAS Space time modelling of Chile induced seismicity using covariates.

<p>Chilean seismic activity is among the strongest ones in the world. As already shown in previous papers, seismic activity can be usefully described by a space-time branching process, like the ETAS (Epidemic Type Aftershock Sequences) model, which is a semiparametric model with a large time scale component for the background seismicity and a small time scale component for the induced seismicity. The large-scale component intensity function  is usually estimated by  nonparametric techniques, specifically in our paper we used the Forward Likelihood Predictive approach (FLP); the induced seismicity is modelled with a parametric space-time function. In c…

Maximum Likelihood Based Declustering of Simulated and Observed Seismic Catalogs

Study of the interaction structure of the East Sicily Seismicity: global and local scale.

Financial contagion through space-time point processes

AbstractWe 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.

Including Covariates in the ETAS Model Triggered Seismicity

The paper proposes a stochastic process that improves the assessment of seismic 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 catalogue is reported, together with the reference to the developed R package.

Earthquakes clustering based on maximum likelihood estimation of point process conditional intensity function

Clustering and Registration of Multidimensional Functional Data

In order to find similarity between multidimensional curves, we consider the application of a procedure that provides a simultaneous assignation to clusters and alignment of such functions. In particular we look for clusters of multivariate seismic waveforms based on EM-type procedure and functional data analysis tools.

Functional data analysis for optimizing strategies of cash flow management

The cash management deals with problem of automating and managing cash flow processes. Optimization of the management processes greatly reduces overall cash handling costs. The present analysis is an empirical study of cash flows, from and to bank branches, deriving an underlying theoretical framework, which can in a reasonable way be connected with the optimal strategy. Functional data analysis is considered an appropriate framework to analyse the dynamics of the time series behavior of cash flows: since the observations are not equally spaced in time and their number is different for each series, they are converted in a collection of random curves in a space spanned by finite dimensional …

Comparison between nonparametric and parametric estimate of the conditional intensity function of a seismic space-time point process

A seismic gap can be defined as a segment of an active geologic fault that has not produced seismic events for an unusually long time; gaps are often considered susceptible to future strong earthquakes occurrence and therefore their identification may be useful for predictive purposes. In this paper we try to identify gaps in an area of South Tyrrhenian Sea by comparing the observed seismicity, estimated by nonparametric method, and the theoretical one, described by a particular space-time point process (ETAS model).

A fast and efficient picking algorithm for earthquake early warning application based on the variance piecewise constant models

An earthquake warning system, or earthquake early warning system, is a system of accelerometers, seismometers, communication, computers, and alarms that is devised for notifying adjoining regions of a substantial earthquake while it is in progress. This is not the same as earthquake prediction, which is currently incapable of producing decisive event warnings. The implementation of efficient and computationally simple picking algorithm is necessary for this purpose, as well as automatic picking of seismic phases for seismic surveillance and routine earthquake location for fast hypocenter determination. In this paper a method for picking based on the detection of signals changes in variance …

A new picking algorithm based on the variance piecewise constant models

AbstractIn this paper, we propose a novel picking algorithm for the automatic P- and S-waves onset time determination. Our algorithm is based on the variance piecewise constant models of the earthquake waveforms. The effectiveness and robustness of our picking algorithm are tested both on synthetic seismograms and real data. We simulate seismic events with different magnitudes (between 2 and 5) recorded at different epicentral distances (between 10 and 250 km). For the application to real data, we analyse waveforms from the seismic sequence of L’Aquila (Italy), in 2009. The obtained results are compared with those obtained by the application of the classic STA/LTA picking algorithm. Althoug…

Penalized classification for optimal statistical selection of markers from high-throughput genotyping: application in sheep breeds

The identification of individuals’ breed of origin has several practical applications in livestock and is useful in different biological contexts such as conservation genetics, breeding and authentication of animal products. In this paper, penalized multinomial regression was applied to identify the minimum number of single nucleotide polymorphisms (SNPs) from high-throughput genotyping data for individual assignment to dairy sheep breeds reared in Sicily. The combined use of penalized multinomial regression and stability selection reduced the number of SNPs required to 48. A final validation step on an independent population was carried out obtaining 100% correctly classified individuals. …

Clustering of waveforms based on FPCA direction

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 clust…

Time-Frequency Filtering for Seismic Waves Clustering

This paper introduces a new technique for clustering seismic events based on processing, in time-frequency domain, the waveforms recorded by seismographs. The detection of clusters of waveforms is performed by a k-means like algorithm which analyzes, at each iteration, the time-frequency content of the signals in order to optimally remove the non discriminant components which should compromise the grouping of waveforms. This step is followed by the allocation and by the computation of the cluster centroids on the basis of the filtered signals. The effectiveness of the method is shown on a real dataset of seismic waveforms.

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

An estimation approach for the semi-param-etric intensity function of a class of space-time point processes is introduced. In particular we want to account for the estimation of parametric and nonparametric components simultaneously, applying a forward predictive likelihood to semi-parametric models. For each event, the probability of being a background event or an offspring is therefore estimated.

Un tentativo di analisi integrata della tettonica e sismicità nella zona di cerniera tra Sicilia settentrionale e Basso Tirreno.

Flexible space-time process for seismic data

Point processes are well studied objects in probability theory and a powerful tool in statistics for modelling and analyzing the distribution of real phenomena, such as the seismic one. Point processes can be specified mathematically in several ways, for instance, by considering the joint distributions of the counts of points in arbitrary sets or defining a complete intensity function. The conditional intensity function is a function of the point history and it is itself a stochastic process depending on the past up to time t. In this paper some techniques to estimate the intensity function of space-time point processes are developed, by following semi-parametric approaches and diagnostic m…

Semi-parametric estimation of the intensity function in space-time point processes

Space-Time Forecasting of Seismic Events in Chile

The aim of this work is to study the seismicity in Chile using the ETAS (epidemic type aftershock sequences) space‐time approach. The proposed ETAS model is estimated using a semi‐parametric technique taking into account the parametric and nonparametric components corresponding to the triggered and background seismicity, respectively. The model is then used to predict the temporal and spatial intensity of events for some areas of Chile where recent large earthquakes (with magnitude greater than 8.0 M) occurred.

Valutazione statistica del carico di studi e della didattica per la gestione di un Corso di Laurea.

Vascular Risk Factors, Vascular Diseases, and Imaging Findings in a Hospital-based Cohort of Mild Cognitive Impairment Types

Background: Mild Cognitive Impairment (MCI) is a transitional state between normal cognition and dementia. Objective: The aim of this study is to investigate the role of vascular risk factors, vascular diseases, cerebrovascular disease and brain atrophy in a large hospital-based cohort of MCI types including 471 amnestic MCI (a-MCI), 693 amnestic MCI multiple domain (a-MCImd), 322 single non-memory MCI (snm-MCI), and 202 non amnestic MCI multiple domain (na-MCImd). For comparison, 1,005 neurologically and cognitively healthy subjects were also evaluated. Method: Several vascular risk factors and vascular diseases were assessed. All participants underwent neurological, neuropsychological an…

Semi-parametric estimation of conditional intensity functions in inhomogeneous space-time point processes

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…

Southern-Tyrrhenian seismicity in space-time-magnitude domain

An analysis is conducted on a catalogue containing more than 2000 seismic events
 occurred in the southern Tyrrhenian Sea between 1988 and October 2002, as an attempt
 to characterise the main seismogenetic processes active in the area in space, time and magnitude domain by means of the parameters of phenomenological laws.
 
 We chose to adopt simple phenomenological models, since the low number of data did
 not allow to use more complex laws.
 
 The two main seismogenetic volumes present in the area were considered for the purpose
 of this work. The first includes a nearly homogeneous distribution of hypocentres in a
 NW steeply dipping layer as far as a…

Scritti scelti di Antonino Mineo

Questo volume presenta una selezione ragionata degli scritti di Antonino Mineo (1936-2008), di cui mantiene la veste tipografica originale. La raccolta di scritti è preceduta dai lavori di Giuseppe Burgio, Marcello Chiodi e Vittorio Frosini, presentati in occasione della “Giornata in ricordo di Antonino Mineo”, tenuta il 22 maggio 2009 presso la Facoltà di Economia di Palermo ed al termine della quale è stata intitolata ad Antonino Mineo un'aula del dipartimento di Scienze Statistiche e Matematiche “Silvio Vianelli”. I lavori di Giuseppe Burgio e Vittorio Frosini sono due contributi originali presentati in quella giornata di studio, sul tema delle curve normali di ordine p, che è stato uno …

Il contributo di Antonino Mineo alla statistica italiana

Antonino Mineo (1936-2008), professore ordinario di Statistica Metodologica, `e stato una figura importante di statistico nel panorama italiano e internazionale. I suoi contributi scientifici sono stati prevalentemente nell’ambito della statistica multivariata, della statistica computazionale e delle distribuzioni di errori accidentali non normali. Fra le attivit`a istituzionali `e stato Preside della Facolt`a di Economia di Palermo, e fondatore del Dipartimento di Scienze Statistiche e Matematiche Silvio Vianelli. In questo lavoro, che costituisce anche una bibliografia ragionata dell’opera di Mineo, vengono presentati i contributi principali di Mineo, inquadrandoli nella loro cornice stor…

Simultaneous seismic wave clustering and registration

In this paper we introduce a simple procedure to identify clusters of multivariate waveforms based on a simultaneous assignation and alignment procedure. This approach is aimed at the identification of clusters of earthquakes, assuming that similarities between seismic events with respect to hypocentral parameters and focal mechanism correspond to similarities between waveforms of events. Therefore we define a distance measure between seismic curves in R^d d>=1, in order to interpret and better understand the main features of the generating seismic process.

A prospective, randomized study of empirical antifungal therapy for the treatment of chemotherapy-induced febrile neutropenia in children

Given that the rationale for empirical antifungal therapy in neutropenic children is limited and based on adult patient data, we performed a prospective, randomized, controlled trial that evaluated 110 neutropenic children with persistent fever. Those at high risk for invasive fungal infections (IFI) received caspofungin (Arm C) or liposomal amphotericinB (Arm B); those with a lower risk were randomized to receive Arm B, C, or no antifungal treatment (Arm A). Complete response to empirical antifungal therapy was achieved in 90/104 patients (86·5%): 48/56 at high risk (85·7%) [88·0% in Arm B; 83·9% in Arm C (P = 0·72)], and 42/48 at low risk (87·5%) [87·5% in control Arm A, 80·0% Arm B, 94·1…

Clustering of seismic catalogs based on maximum likelihood estimation

Probabilistic forecast for Northern New Zealand seismic process: a kernel-based approach

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.

Probabilistic Forecast for Northern New Zealand Seismic Process Based on a Forward Predictive Kernel Estimator

In seismology predictive properties of the estimated intensity function are often pursued. For this purpose, we propose an estimation procedure in time, longitude, latitude and depth domains, based on the subsequent increments of likelihood obtained adding an observation one at a time. On the basis of this estimation approach a forecast of earthquakes of a given area of Northern New Zealand is provided, assuming that future earthquakes activity may be based on the smoothing of past earthquakes.

ConvLSTM Neural Networks for seismic event prediction in Chile

Predicting seismic risk is a challenging task in order to avoid catastrophic effects. In this work, two models based on Convolutional Network (CNN) and Long Short Term Memory (LSTM) networks are proposed to predict the seismic risk in Chile. In particular, a ConvLSTM and a Multi-column ConvLSTM network are used for the prediction of the average number of seismic events greater than 2,8 magnitude on the Richter scale, in the Chilean regions of Coquimbo and Araucania between the years 2010 and 2017. For this model, the values of the intensity function estimated through an ETAS model and the accumulated displacement prior to a the seismic events are used as inputs. In particular, given the spa…

Comparative analysis of the thermal insulation performance of a façade enclosure integrated by vegetation under simultaneous windy and rainy climatic conditions

The literature offers some studies on the capacity of the greenery apparatus to decrease wind speed and regulate temperatures with the combination of the moisture retained by the plants and the air passing through them, but there is little on the maintenance of performance under particular weather conditions. The aim of this contri- bution is to verify the effectiveness of a vegetal façade in particularly windy conditions combined with rainy and/or high-irradiation events. The subject of the study is the enclosure of the Technology Innovation Centre for Development (itdUPM), on the Polytechnic University of Madrid, where a green wall prototype has been installed. For the purposes of the ana…

Space-Time FPCA Clustering of Multidimensional Curves.

In this paper we focus on finding clusters of multidimensional curves with spatio-temporal structure, applying a variant of a k-means algorithm based on the principal component rotation of data. The main advantage of this approach is to combine the clustering functional analysis of the multidimensional data, with smoothing methods based on generalized additive models, that cope with both the spatial and the temporal variability, and with functional principal components that takes into account the dependency between the curves.

Identification of breed informative single nucleotide polymorphisms for assigning individual in Sicilian dairy sheep

Assignment tests using genetic information to establish population membership of individuals, provide the most direct methods to determine the population of origin of unknown individuals. The identification of the breed or population of origin of individuals potentially offers unbiased tools in livestock and is useful in a variety of biological contexts. The aim of this study was to identify the minimum number of informative SNPs from highthroughput genotyping data in Sicilian dairy sheep breeds, and to investigate their usefulness for breed assignment purposes. Individuals of Valle del Belice (48), Comisana (48) and Pinzirita (53) sheep breeds were genotyped using Illumina OvineSNP50K Geno…

Approximations to the Likelihood in Composite Samples with Fixed and Random Weights

Towards the specification of a self-exciting point process for modelling crimes in Valencia

A number of papers have dealt with the analysis of crime data using self-exciting point process theory after the analogy drawn between aftershock ETAS models and crime rate. With the aim to describe crime events that occurred in Valencia in the last decade, in this paper, we justify the need for a self-exciting point process model through spatial and temporal exploratory analysis.

Statistical Picking of Multivariate Waveforms

In this paper, we propose a new approach based on the fitting of a generalized linear regression model in order to detect points of change in the variance of a multivariate-covariance Gaussian variable, where the variance function is piecewise constant. By applying this new approach to multivariate waveforms, our method provides simultaneous detection of change points in functional time series. The proposed approach can be used as a new picking algorithm in order to automatically identify the arrival times of P- and S-waves in different seismograms that are recording the same seismic event. A seismogram is a record of ground motion at a measuring station as a function of time, and it typica…

Space-time FPCA Algorithm for clustering of multidimensional curves.

In this paper we focus on finding clusters of multidimensional curves with spatio-temporal structure, applying a variant of a k-means algorithm based on the principal component rotation of data. The main advantage of this approach is to combine the clustering functional analysis of the multidimensional data, with smoothing methods based on generalized additive models, that cope with both the spatial and the temporal variability, and with functional principal components that takes into account the dependency between the curves.

Semiparametric estimation of conditional intensity functions for space-time processes

When dealing with data coming from a space time inhomogeneous process, there is often the need of obtaining reliable estimates of the conditional intensity function. According to the field of application, intensity function can be estimated through some assessed parametric model, where parameters are estimated by Maximum Likelihood method. If we are only in an exploratory context or we would like to assess the adequacy of the parametric model, some kind of nonparametric estimation is required. Often, isotropic or anisotropic kernel estimates can be used, e.g. using the Silverman rule for the choice of the windows sizes h (Silverman, 1986). When the purpose of the study is the estimation of …

Un metodo per l'identificazione di cluster di eventi sismici

A method to identify clusters of seismic events