Spatial and spatio-temporal log-Gaussian Cox processes: extending the geostatistical paradigm
From MaRDI portal
Publication:5965041
DOI10.1214/13-STS441zbMath1331.86027arXiv1312.6536OpenAlexW2041722960WikidataQ58851835 ScholiaQ58851835MaRDI QIDQ5965041
Benjamin M. Taylor, Barry Rowlingson, Peter J. Diggle, Paula Moraga
Publication date: 2 March 2016
Published in: Statistical Science (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1312.6536
Inference from spatial processes (62M30) Geostatistics (86A32) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55)
Related Items
Spatial Cox processes in an infinite-dimensional framework, Improving the usability of spatial point process methodology: an interdisciplinary dialogue between statistics and ecology, Vecchia-Laplace approximations of generalized Gaussian processes for big non-Gaussian spatial data, A review of self-exciting spatio-temporal point processes and their applications, Multivariate geometric anisotropic Cox processes, Conditional intensity: A powerful tool for modelling and analysing point process data, Approximate Bayesian inference for multivariate point pattern analysis in disease mapping, A combined statistical and machine learning approach for spatial prediction of extreme wildfire frequencies and sizes, Fast, Approximate Maximum Likelihood Estimation of Log-Gaussian Cox Processes, A randomized multi-index sequential Monte Carlo method, Bootstrap Confidence Regions for Learned Feature Embeddings, A log-Gaussian Cox process with sequential Monte Carlo for line narrowing in spectroscopy, Log-Gaussian Cox processes in infinite-dimensional spaces, Scalable inference for space‐time Gaussian Cox processes, Locally weighted minimum contrast estimation for spatio-temporal log-Gaussian Cox processes, sppmix: Poisson point process modeling using normal mixture models, Combining heterogeneous spatial datasets with process-based spatial fusion models: a unifying framework, Spatial and spatio-temporal log-Gaussian Cox processes: extending the geostatistical paradigm, Scalable high-resolution forecasting of sparse spatiotemporal events with kernel methods: a winning solution to the NIJ ``Real-time crime forecasting challenge, Bayesian P-splines and advanced computing in R for a changepoint analysis on spatio-temporal point processes, A three-step local smoothing approach for estimating the mean and covariance functions of spatio-temporal data, Space-time inhomogeneous background intensity estimators for semi-parametric space-time self-exciting point process models, Spatiotemporal point processes: regression, model specifications and future directions, Local spatial log-Gaussian Cox processes for seismic data
Uses Software
Cites Work
- An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach
- Longitudinal data analysis using generalized linear models
- Recent developments on the construction of spatio-temporal covariance models
- Model-based geostatistics.
- Improving the performance of predictive process modeling for large datasets
- Spatial variation. 2nd ed
- Bayesian image restoration, with two applications in spatial statistics (with discussion)
- Exponential convergence of Langevin distributions and their discrete approximations
- Families of spatio-temporal stationary covariance models.
- A close look at the spatial structure implied by the CAR and SAR models.
- Optimal scaling for various Metropolis-Hastings algorithms.
- Spatiotemporal Prediction for Log-Gaussian Cox Processes
- Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations
- A Class of Convolution-Based Models for Spatio-Temporal Processes with Non-Separable Covariance Structure
- Handbook of Spatial Statistics
- Gaussian Predictive Process Models for Large Spatial Data Sets
- Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
- The second-order analysis of stationary point processes
- Log Gaussian Cox Processes
- Non‐ and semi‐parametric estimation of interaction in inhomogeneous point patterns
- Nonseparable, Stationary Covariance Functions for Space–Time Data
- Combining Incompatible Spatial Data
- Modeling Spatial Variation in Disease Risk
- Blur-generated non-separable space–time models
- Classes of Nonseparable, Spatio-Temporal Stationary Covariance Functions
- Spatial Poisson Regression for Health and Exposure Data Measured at Disparate Resolutions
- Gaussian Markov Random Fields
- Approximate Inference in Generalized Linear Mixed Models
- Coupling and Ergodicity of Adaptive Markov Chain Monte Carlo Algorithms
- Inconsistent Estimation and Asymptotically Equal Interpolations in Model-Based Geostatistics
- Monte Carlo sampling methods using Markov chains and their applications
- The spectral analysis of two-dimensional point processes
- Nonparametric Estimation of Spatial Segregation in a Multivariate Point Process: Bovine Tuberculosis in Cornwall, UK
- Spatial and spatio-temporal log-Gaussian Cox processes: extending the geostatistical paradigm
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item