Spatial models for point and areal data using Markov random fields on a fine grid
From MaRDI portal
Publication:1951143
DOI10.1214/13-EJS791zbMath1337.62302arXiv1204.6087MaRDI QIDQ1951143
Publication date: 29 May 2013
Published in: Electronic Journal of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1204.6087
Inference from spatial processes (62M30) Random fields; image analysis (62M40) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Gaussian processes (60G15) Applications of statistics to environmental and related topics (62P12)
Related Items (2)
Spatial models for point and areal data using Markov random fields on a fine grid ⋮ Interpolation of precipitation extremes on a large domain toward IDF curve construction at unmonitored locations
Uses Software
Cites Work
- An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach
- Spline smoothing: The equivalent variable kernel method
- A comparative study of Gaussian geostatistical models and Gaussian Markov random field models
- A stochastic neighborhood conditional autoregressive model for spatial data
- Practical large-scale spatio-temporal modeling of particulate matter concentrations
- Interpolation of spatial data. Some theory for kriging
- A close look at the spatial structure implied by the CAR and SAR models.
- Spatial models for point and areal data using Markov random fields on a fine grid
- Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations
- Bayesian Prediction of Spatial Count Data Using Generalized Linear Mixed Models
- Geoadditive Models
- First-order intrinsic autoregressions and the de Wijs process
- Gaussian Predictive Process Models for Large Spatial Data Sets
- Modeling Spatial Variation in Disease Risk
- Directional Rates of Change Under Spatial Process Models
- A Geostatistical Approach to Large‐Scale Disease Mapping with Temporal Misalignment
- Approximating Likelihoods for Large Spatial Data Sets
- Gaussian Markov Random Fields
- Approximate Inference in Generalized Linear Mixed Models
- Generalized linear mixed models a pseudo-likelihood approach
- Hierarchical Spatial Process Models for Multiple Traits in Large Genetic Trials
- Covariance Tapering for Likelihood-Based Estimation in Large Spatial Data Sets
- Understanding Gaussian Process Regression Using the Equivalent Kernel
- Model Evaluation and Spatial Interpolation by Bayesian Combination of Observations with Outputs from Numerical Models
This page was built for publication: Spatial models for point and areal data using Markov random fields on a fine grid
