The following pages link to Jorge Mateu (Q65811):
Displaying 39 items.
- (Q5402393) (← links)
- (Q5422040) (← links)
- Geostatistical Analysis Through Spectral Techniques: Some Words of Caution (Q5436421) (← links)
- On Random and Gibbsian Particle Motions for Point Processes Evolving in Space and Time (Q5451150) (← links)
- Estimating Mark Functions Through Spectral Analysis for Marked Point Patterns (Q5484672) (← links)
- Jorge Mateu’s Contribution to the Discussion of ‘Gaussian Differential Privacy’ by Dong <i>et al.</i> (Q5869179) (← links)
- Pseudo-likelihood inference for Gibbs processes with expontential families through generalized linear models (Q5952138) (← links)
- Beta spatial linear mixed model with variable dispersion using Monte Carlo maximum likelihood (Q6063610) (← links)
- Identifying crime generators and spatially overlapping high‐risk areas through a nonlinear model: A comparison between three cities of the Valencian region (Spain) (Q6067783) (← links)
- Fast Kernel Smoothing of Point Patterns on a Large Network using Two‐dimensional Convolution (Q6086586) (← links)
- Analysing Multivariate Spatial Point Processes with Continuous Marks: A Graphical Modelling Approach (Q6086605) (← links)
- Assessing similarities between spatial point patterns with a siamese neural network discriminant model (Q6106143) (← links)
- Testing similarity between first-order intensities of spatial point processes. A comparative study (Q6116999) (← links)
- A nonseparable first-order spatiotemporal intensity for events on linear networks: an application to ambulance interventions (Q6128443) (← links)
- A mechanistic spatio‐temporal modeling of COVID‐19 data (Q6149264) (← links)
- On the trend detection of time-ordered intensity images of point processes on linear networks (Q6171857) (← links)
- Optimal dynamic spatial sampling (Q6179636) (← links)
- Directional analysis for point patterns on linear networks (Q6541714) (← links)
- A mechanistic bivariate point process model for crime pattern analysis (Q6548776) (← links)
- Second-order preserving point process permutations (Q6548798) (← links)
- A spatio-temporal Dirichlet process mixture model for coronavirus disease-19 (Q6560559) (← links)
- Jorge Mateu's contribution to the discussion of `the Discussion Meeting on Probabilistic and statistical aspects of machine learning' (Q6569520) (← links)
- A third-order point process characteristic for multi-type point processes (Q6573255) (← links)
- ANOVA for metric spaces, with applications to spatial data (Q6577812) (← links)
- High leverage detection in general functional regression models with spatially correlated errors (Q6580697) (← links)
- Hierarchical Spatio-Temporal Change-Point Detection (Q6585601) (← links)
- A first-order, ratio-based nonparametric separability test for spatiotemporal point processes (Q6625888) (← links)
- Testing for local structure in spatiotemporal point pattern data (Q6626002) (← links)
- Spatio-temporal classification in point patterns under the presence of clutter (Q6626135) (← links)
- Space-time autoregressive estimation and prediction with missing data based on Kalman filtering (Q6626174) (← links)
- Spatially informed Bayesian neural network for neurodegenerative diseases classification (Q6629915) (← links)
- Analysis of multispecies point patterns by using multivariate log-Gaussian Cox processes (Q6639105) (← links)
- Analysis of tornado reports through replicated spatiotemporal point patterns (Q6642151) (← links)
- Hierarchical clustering of spatially correlated functional data (Q6647321) (← links)
- Non-stationary spatio-temporal point process modeling for high-resolution COVID-19 data (Q6662874) (← links)
- Jorge Mateu's contribution to the discussion of `Flexible marked spatio-temporal point processes with applications to event sequences from association football' by Narayanan, Kosmidis, and Dellaportas (Q6662948) (← links)
- Point process modeling through a mixture of homogeneous and self-exciting processes (Q6668594) (← links)
- A semiparametric spatiotemporal Hawkes-type point process model with periodic background for crime data (Q6668788) (← links)
- Self-exciting point process modelling of crimes on linear networks (Q6669957) (← links)
