A spatio-temporal downscaler for output from numerical models
From MaRDI portal
Publication:2260144
DOI10.1007/s13253-009-0004-zzbMath1306.62243OpenAlexW2060517955WikidataQ34342478 ScholiaQ34342478MaRDI QIDQ2260144
Veronica J. Berrocal, Alan E. Gelfand, David M. Holland
Publication date: 5 March 2015
Published in: Journal of Agricultural, Biological, and Environmental Statistics (Search for Journal in Brave)
Full work available at URL: http://europepmc.org/articles/pmc2990198
Markov chain Monte Carlocalibrationordinary krigingBayesian meldingspatially varying coefficient modelspatial misalignment
Related Items
A spectral method for spatial downscaling, Uncertainty Quantification Using the Nearest Neighbor Gaussian Process, A directionally varying change points model for quantifying the impact of a point source, Multivariate functional data modeling with time-varying clustering, Geostatistical Modelling Using Non-Gaussian Matérn Fields, Spatiotemporal exposure prediction with penalized regression, Modeling 3‐D spatio‐temporal biogeochemical processes with a forest of 1‐D statistical emulators, Assessing exceedance of ozone standards: a space‐time downscaler for fourth highest ozone concentrations, Functional single-index quantile regression models, Heterogeneous Distributed Lag Models to Estimate Personalized Effects of Maternal Exposures to Air Pollution, Controlling for unmeasured confounding and spatial misalignment in long‐term air pollution and health studies, A data fusion approach for spatial analysis of speciated PM2.5 across time, Identifying trends in the spatial errors of a regional climate model via clustering, A comparison of centring parameterisations of Gaussian process-based models for Bayesian computation using MCMC, Extreme value analysis for evaluating ozone control strategies, Estimating the health impact of climate change with calibrated climate model output, Spatial distributed lag data fusion for estimating ambient air pollution, Combining heterogeneous spatial datasets with process-based spatial fusion models: a unifying framework, Spatial Statistical Data Fusion for Remote Sensing Applications, Space‐Time Data fusion Under Error in Computer Model Output: An Application to Modeling Air Quality, A bivariate space-time downscaler under space and time misalignment, Quantifying local creation and regional transport using a hierarchical space-time model of ozone as a function of observed \(\text{NO}_{\text{x}}\), a latent space-time VOC process, emissions, and meteorology, Calibration of numerical model output using nonparametric spatial density functions, Attaching uncertainty to deterministic spatial interpolations, Hierarchical spatially varying coefficient and temporal dynamic process models usingspTDyn, Dynamically Updated Spatially Varying Parameterizations of Hierarchical Bayesian Models for Spatial Data, A class of covariate-dependent spatiotemporal covariance functions for the analysis of daily ozone concentration, Complete spatial model calibration, Data fusion model for speciated nitrogen to identify environmental drivers and improve estimation of nitrogen in lakes, A spatial causal analysis of wildland fire-contributed \(\mathrm{PM}_{2.5}\) using numerical model output, Statistical downscaling with spatial misalignment: application to wildland fire \(\mathrm{PM}_{2.5}\) concentration forecasting
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
- Bayesian forecasting and dynamic models.
- A statistical framework to combine multivariate spatial data and physical models for hurricane surface wind prediction
- Nonstationary multivariate process modeling through spatially varying coregionalization
- Spatial Association between Speciated Fine Particles and Mortality
- Automatic Bayesian Curve Fitting
- Model-Based Geostatistics
- On Gibbs sampling for state space models
- Air Pollution and Mortality
- Combining Incompatible Spatial Data
- Spatial Modeling With Spatially Varying Coefficient Processes
- Inference for Deterministic Simulation Models: The Bayesian Melding Approach
- Inconsistent Estimation and Asymptotically Equal Interpolations in Model-Based Geostatistics
- Model Evaluation and Spatial Interpolation by Bayesian Combination of Observations with Outputs from Numerical Models
