Listar por autoría "95b3546d-e344-4f88-ae0a-9c54e7781f1f"
Mostrando ítems 1-20 de 189
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2D Anisotropic Wavelet Entropy with an Application to Earthquakes in Chile
Nicolis, Orietta; Mateu, Jorge MDPI (2015-06-16)We propose a wavelet-based approach to measure the Shannon entropy in the context of spatial point patterns. The method uses the fully anisotropic Morlet wavelet to estimate the energy distribution at different directions ... -
A Bayesian machine learning approach for spatio-temporal prediction of COVID-19 cases
Niraula, Poshan; Mateu, Jorge; Chaudhuri, Somnath Springer (2022)Modeling the spread of infectious diseases in space and time needs to take care of complex dependencies and uncertainties. Machine learning methods, and neural networks, in particular, are useful in modeling this sort of ... -
A Bayesian Spatial Analysis of the Heterogeneity in Human Mobility Changes During the First Wave of the COVID-19 Epidemic in the United States
Carella, Giulia; Pérez Trufero, Javier; Álvarez, Miguel; Mateu, Jorge American Statistician (2021)The spread of COVID-19 in the U.S. prompted nonpharmaceutical interventions which caused a reduction in mobility everywhere, although with large disparities between different counties. Using a Bayesian spatial modeling ... -
A conditional machine learning classification approach for spatio-temporal risk assessment of crime data
Rodrigues, Alexandre; González, Jonatan A.; Mateu, Jorge Springer (2023)Crime data analysis is an essential source of information to aid social and political decisions makers regarding the allocation of public security resources. Computer-aided dispatch systems and technological advances in ... -
A continuous wavelet-based approach to detect anisotropic properties in spatial point processes
D'Ercole, Roberto; Mateu, Jorge World Scientific Publishing (2013)A two-dimensional stochastic point process can be regarded as a random measure and thus represented as a (countable) sum of Delta Dirac measures concentrated at some points. Integration with respect to the point process ... -
A Distance-based Method for Spatial Prediction in the Presence of Trend
Melo, Carlos E.; Mateu, Jorge; Melo, Oscar O. Springer (2020-06-01)A new method based on distances for modeling continuous random data in Gaussian random fields is presented. In non-stationary cases in which a trend or drift is present, dealing with information in regionalized mixed ... -
A distance-based model for spatial prediction using radial basis functions
Melo, Carlos E.; Melo, Oscar O.; Mateu, Jorge Springer Berlin Heidelberg (2018)In the context of local interpolators, radial basis functions (RBFs) are known to reduce the computational time by using a subset of the data for prediction purposes. In this paper, we propose a new distance-based spatial ... -
A Dynamic Spatio-Temporal Stochastic Modeling Approach of Emergency Calls in an Urban Context
Payares García, David Enrique; Platero Puig, Javier; Mateu, Jorge MDPI (2023)Emergency calls are defined by an ever-expanding utilisation of information and sensing technology, leading to extensive volumes of spatio-temporal high-resolution data. The spatial and temporal character of the emergency ... -
A dynamical mathematical model for crime evolution based on a compartmental system with interactions
Calatayud, Julia; Jornet, Marc; Mateu, Jorge Taylor & Francis (2024-01-11)We use data on imprisonment in Spain to fit a system of three ordinary differential equations that describes the temporal evolution of three different groups in the country: offenders that are not in prison, offenders that ... -
A family of Markov processes in maximal compact subgroups of a semisimple Lie groups
Arafat, Ahmed; Mateu, Jorge; Gregori, Pablo Elsevier (2017)We propose and define a family of marked point processes in noncompact semisimple Lie groups. We first generate Lévy processes via marked point processes by using jump–diffusion processes. Then we build a family of Markov ... -
A first‐order, ratio‐based nonparametric separability test for spatiotemporal point processes
Fuentes Santos, I.; González-Manteiga, Wenceslao; Mateu, Jorge (2018-02)Testing whether the intensity function of a spatiotemporal point process is sep- arable should be one of the first steps in the analysis of any observed pattern. Under separability, the risk of observing an event at time ... -
A hierarchical spatio-temporal model to analyze relative risk variations of COVID-19: a focus on Spain, Italy and Germany
Jalilian, Abdollah; Mateu, Jorge Springer-Verlag GmbH Germany, part of Springer Nature (2021-04-23)The novel coronavirus disease (COVID-19) has spread rapidly across the world in a short period of time and with a heterogeneous pattern. Understanding the underlying temporal and spatial dynamics in the spread of COVID-19 ... -
A hierarchical spatio-temporal model to analyze relative risk variations of COVID-19: a focus on Spain, Italy and Germany
Jalilian, Abdollah; Mateu, Jorge Springer (2021)The novel coronavirus disease (COVID-19) has spread rapidly across the world in a short period of time and with a heterogeneous pattern. Understanding the underlying temporal and spatial dynamics in the spread of COVID-19 ... -
A Kalman filter method for estimation and prediction of space–time data with an autoregressive structure
Lagos Álvarez, Bernardo M.; Padilla-Solis, Leonardo Fabricio; Mateu, Jorge; Ferreira, Guillermo Elsevier (2019-03)We propose a new Kalman filter algorithm to provide a formal statistical analysis of space–time data with an autoregressive structure. The Kalman filter technique allows to capture the temporal dependence as well as the ... -
A local correlation integral method for outlier detection in spatially correlated functional data
Sosa, Jorge; Moraga, Paula; Flores, MIguel; Mateu, Jorge Springer (2023)This paper proposes a new methodology for detecting outliers in spatially correlated functional data. We use a Local Correlation Integral (LOCI) algorithm substituting the Euclidean distance calculation by the Hilbert space ... -
A mechanistic bivariate point process model for crime pattern analysis
Briz-Redón, Álvaro; Mateu, Jorge Wiley (2023)The statistical analysis of crime data has gained attention in the last decade. In particular, the availability of spatio-temporal crime data at the event level allows us to model the incidence of crime with high precision. ... -
A mechanistic spatio-temporal modeling of COVID-19 data
Briz-Redón, Álvaro; Iftimi, Adina; Mateu, Jorge; Romero Garcia, Carolina Soledad Wiley-VCHGmbH (2022-08-07)Understandingtheevolutionofanepidemicisessentialtoimplementtimelyandefficient preventive measures. The availability of epidemiological data at a finespatio-temporal scale is both novel and highly useful in this regard. ... -
A new population model for urban infestations
Calatayud, Julia; Jornet, Marc; Mateu, Jorge; Pinto, Carla M. A. Elsevier (2023-10-10)The spread of rodents and insects in cities, in particular in summer periods, poses significant health, economic, social and environmental threats. The analysis of incidence and identification of seasonal and weather ... -
A nonparametric test for the comparison of first-order structures of spatial point processes
Fuentes Santos, I.; González-Manteiga, Wenceslao; Mateu, Jorge Elsevier (2017)Comparing the spatial distribution of two spatial point patterns is an important issue in many scientific areas such as ecology, epidemiology or environmental risk assessment. However, up to date, the analysis of multitype ... -
A nonseparable first-order spatiotemporal intensity for events on linear networks: An application to ambulance interventions
Gilardi, Andrea; Borgoni, Riccardo; Mateu, Jorge Institute of Mathematical Statistics (2024-03)The algorithms used for the optimal management of an ambulance fleet require an accurate description of the spatiotemporal evolution of the emergency events. In the last years, several authors have proposed sophisticated ...