Listar INIT_Articles por autoría "95b3546d-e344-4f88-ae0a-9c54e7781f1f"
Mostrando ítems 1-20 de 134
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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 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 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 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 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 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 note on smoothness measures for space-time surfaces
Bohorquez Castañeda, Martha Patricia; Mateu, Jorge; Díaz, Laura Springer Verlag (2013-09)The differentiability of a random field has a direct relationship with the differentiability of its covariance function. We review the concept of differentiability of space–time covariance models and random fields, and its ... -
A penalized likelihood method for nonseparable space–time generalized additive models
Mosammam, Ali M.; Mateu, Jorge Springer Verlag (2018-07)In this paper, we study space-time generalized additive models. We apply the penalyzed likelihood method to fit generalized additive models (GAMs) for nonseparable spatio-temporal correlated data in order to improve the ... -
A second-order test to detect spatio-temporal anisotropic effects in point patterns
Comas Rodríguez, Carlos; Conde, J.; Mateu, Jorge Taylor & Francis (2018)Second-order spatio-temporal orientation methods provide a natural tool for the analysis of anisotropic spatio-temporal point process data. In this paper, we generalize a method based on the spatial point pair orientation ... -
A semiparametric spatiotemporal Hawkes-type point process model with periodic background for crime data
Zhuang, Jiancang; Mateu, Jorge Wiley (2018-12)Past studies have shown that crime events are often clustered. This study proposes a spatiotemporal Hawkes-type point process model, which includes a background component with daily and weekly periodization, and a ... -
A simple two-step method for spatio-temporal design-based balanced sampling
Khavarzadeh, Ramin; Mohammadzadeh, Mohsen; Mateu, Jorge Springer Berlin Heidelberg (2017)We introduce a two-step method to perform spatio-temporal balanced sampling in a design-based approach. For populations with spatio-temporal trends and with anisotropic effects in the variable of interest, the prediction ... -
A spatial functional count model for heterogeneity analysis in time
Torres Signes, Antoni; Frías Bustamante, María del Pilar; Mateu, Jorge; Ruiz-Medina, M. D. Springer (2021)A spatial curve dynamical model framework is adopted for functional prediction of counts in a spatiotemporal log-Gaussian Cox process model. Our spatial functional estimation approach handles both wavelet-based heterogeneity ... -
A spatial pattern analysis of the halophytic species distribution in an arid coastal environment
Badreldin, Nasem; Uría Díez, Jaime; Mateu, Jorge; Youssef, Ali; Stal, Cornelis; El-Bana, Magdy; Magdy, Ahmed; Goossens, Rudi Springer Verlag (2015)Obtaining information about the spatial distribution of desert plants is considered as a serious challenge for ecologists and environmental modeling due to the required intensive field work and infrastructures in harsh and ... -
A spatial randomness test based on the box-counting dimension
Caballero, Yolanda; Giraldo, Ramón; Mateu, Jorge Springer (2022)Statistical modelling of a spatial point pattern often begins by testing the hypothesis of spatial randomness. Classical tests are based on quadrat counts and distance-based methods. Alternatively, we propose a new statistical ...