277 research outputs found
Scritti scelti di Antonino Mineo
No full textQuesto 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 dei temi rilevanti del lavoro scientifico di Mineo; il lavoro di Marcello Chiodi costituisce una rassegna dell'opera scientifica di Antonino Mineo nel panorama della statistica italiana
Including covariates in a space-time point process with application to seismicity
No full textThe 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
Marx's analysis of the relationship between the rate of interest and the rate of profits
No full textClustering 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)
Severe convective storms’ reproduction: empirical analysis from the marked self-exciting point processes point of view
No full textThe paper focuses on the evaluation of hailstorms’ and thunderstorms winds’ events in the United States of America, in the period from 1996 to 2022, under the marked spatio-temporal self-exciting point processes point of view. The aim of the present article is the assessment and description of the spatio-temporal spontaneous and reproducing activity of severe hailstorms’ and thunderstorms winds’ processes. The present application shows how the spatio-temporal pattern is well-fitted and clearly explainable, according to the flexible semi-parametric ETAS model fitting
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
Mixed Non-Parametric and Parametric Estimation Techniques in R Package etasFLP for Earthquakes' Description
No full textetasFLP 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
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
Seismic events classification through latent class regression models for point processes
No full textWe are trying to identify sub-processes of seismic events from the point processes’ point of view and according to the latent class regression approach. Each seismic event is classified as membership of one of the 4 identified sub-classes of seismic sequences, each defined by particular and well-defined characteristics. So far, seismic sub-sequences have been identified and described according to several declustering methods. In this application, we show how sub-processes can be identified starting from the definition of a spatio-temporal intensity function for point processes, assuming independence of the past
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