The kriged Kalman filter. (With discussion)
From MaRDI portal
Publication:1305249
DOI10.1007/BF02565111zbMath0935.62107OpenAlexW1982283335MaRDI QIDQ1305249
Edwin J. Redfern, Kanti V. Mardia, Colin R. Goodall, Francisco Javier Alonso
Publication date: 8 May 2000
Published in: Test (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/bf02565111
krigingEM algorithmKalman filterpollutionstate-space modelbending energyKarahunen-Loeve expansionsspatial-temporal modelling
Inference from stochastic processes and prediction (62M20) Inference from spatial processes (62M30) Applications of statistics to environmental and related topics (62P12) Inference from stochastic processes (62M99)
Related Items
A conversation with Kanti Mardia, Spatio-temporal statistical analysis of the carbon budget of the terrestrial ecosystem, Multifractality in space-time statistical models, Longitudinal analysis of spatially correlated data, Estimation of parameterized spatio-temporal dynamic models, Hierarchical Models in Environmental Science, Sequential Data Assimilation Techniques in Oceanography, A general science-based framework for dynamical spatio-temporal models, Penalized model-based clustering of complex functional data, Robust estimation of a dynamic spatio-temporal model with structural change, A dynamic spatial filtering approach to mitigate underestimation bias in field calibrated low-cost sensor air pollution data, Factor Models for High-Dimensional Tensor Time Series, Spatio-temporal analysis with short- and long-memory dependence: a state-space approach, Parallel inference for massive distributed spatial data using low-rank models, Spatially varying dynamic coefficient models, Gaussian processes on the support of cylindrical surfaces, with application to periodic spatio-temporal data, A Measurement Fusion Method for Nonlinear System Identification Using a Cooperative Learning Algorithm, The analysis of marked point patterns evolving through space and time, Disentangling mark/point interaction in marked-point processes, Model comparison and selection for stationary space-time models, A Kalman filter method for estimation and prediction of space-time data with an autoregressive structure, A reparametrization approach for dynamic space-time models, Spatio‐temporal smoothing and EM estimation for massive remote‐sensing data sets, A kernel-based spectral model for non-Gaussian spatio-temporal processes, Cooperative adaptive sampling of random fields with partially known covariance, A Latent Gaussian Markov Random-Field Model for Spatiotemporal Rainfall Disaggregation, Discussing the ``big \(n\) problem
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- AN APPROACH TO TIME SERIES SMOOTHING AND FORECASTING USING THE EM ALGORITHM
- Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
- Alternative algorithms for the estimation of dynamic factor, mimic and varying coefficient regression models
- Bayesian forecasting and dynamic models
- Modelling longitudinal and spatially correlated data: methods, applications, and future directions. Proceedings of the conference, Nantucket, USA, October 1996
- Principal warps: thin-plate splines and the decomposition of deformations
- Maximum likelihood estimation of models for residual covariance in spatial regression
- Kriging and splines with derivative information
- Maximum Likelihood Fitting of ARMA Models to Time Series with Missing Observations
- Computational aspects of maximum likelihood estimation and reduction in sensitivity function calculations
- Model-Based Geostatistics
- Monte Carlo maximum likelihood estimation for non-Gaussian state space models
- A dimension-reduced approach to space-time Kalman filtering