Space‐Time Data fusion Under Error in Computer Model Output: An Application to Modeling Air Quality
From MaRDI portal
Publication:4649060
DOI10.1111/j.1541-0420.2011.01725.xzbMath1271.62274OpenAlexW2016183155WikidataQ34115505 ScholiaQ34115505MaRDI QIDQ4649060
Alan E. Gelfand, Veronica J. Berrocal, David M. Holland
Publication date: 19 November 2012
Published in: Biometrics (Search for Journal in Brave)
Full work available at URL: http://europepmc.org/articles/pmc4442701
smoothingdata fusionchange of supportGaussian Markov random fieldsnumerical model calibrationspatially varying random weights
Random fields; image analysis (62M40) Applications of statistics to environmental and related topics (62P12) Computing methodologies and applications (68U99)
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Uses Software
Cites Work
- Unnamed Item
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- Unnamed Item
- A bivariate space-time downscaler under space and time misalignment
- Bayesian forecasting and dynamic models.
- Bayesian computation and stochastic systems. With comments and reply.
- A spatio-temporal downscaler for output from numerical models
- Nonstationary multivariate process modeling through spatially varying coregionalization
- Gaussian Predictive Process Models for Large Spatial Data Sets
- High-Resolution Space–Time Ozone Modeling for Assessing Trends
- On Gibbs sampling for state space models
- Combining Incompatible Spatial Data
- Statistics for Spatial Data
- 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
- Bayesian inference for directional conditionally autoregressive models