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

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 storic

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

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

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

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 Gordaliza (2005), Tarpey (2007), Sangalli et al. (2008)

Functional Principal Components direction to cluster earthquake

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 (García-Escudero and Gordaliza (2005), Tarpey (2007), Sangalli et al. (2008))

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 about 400 km depth. This is probably the seismological expression of the Ionian lithospheric slab subducting beneath the Calabrian Arc. The second contains hypocentres concentrated about a sub-horizontal plane lying atan average depth of about 10 km. It is characterised by a background seismicity spread all over the area and by clusters of events that generally show a direction of maximum elongation. The parameters of the models describing seismogenetically homogeneous subsets of the earthquake catalogue in the three analysis domains, along with their confidence intervals, are estimated and analysed to establish whether they can be regarded as representative of a particular subset

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 analysis, the environmental variables are examined and the monitoring data received from sensors positioned at the walls and skin of the insulated envelope are compared with the green face and without, comparing the differences in surface temperatures. These analyses are further examined by considering the correlation with different weather conditions. Experimentation shows a maintenance of per- formance, retaining an insulating capacity in all seasons, in both wind and rain, with results more evident in daylight hours. This contribute want to analyse the subtle variance between the performance of south and west facades. The strongest effect came forward during the summer season because the wall is affected by continuous irradiation on the south that is, also increased by hot weather