Hierarchical Bayesian space-time models

By Christopher K. Wikle, L. M. Berliner, Noel Cressie. 1998

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Citations in the corpus

Kernels for Vector-Valued Functions: a Review (2011, background): “Some of those extensions include the construction of nonstationary covariances [43, 45, 30, 31, 73] and spatiotemporal covariances [109, 107, 108].”
A HIERARCHICAL SPATIAL MODEL OF AVIAN ABUNDANCE WITH APPLICATION TO CERULEAN WARBLERS (2004, background): “Hierarchical Bayesian models are well-suited to the analysis of complicated spatial problems (Wikle et al. 1998).”
Multivariate Bayesian analysis of atmosphere–ocean general circulation models (2007, methodology): “The physical processes driving the climatological responses are extremely complex and it would be virtually impossible to pursue a physical prior modeling approach as illustrated for example in Wikle et al. (2001). Fuentes and Raftery (2005) present an approach where they treat model output and obse”
Particle filters for state-space models with the presence of unknown static parameters (2002, background): “DYNAMIC state-space models [1]–[3] are useful for describing data in many different areas, for instance, engineering [4], finance mathematics [5], environmental data [6], geophysical science [7], and disease data [8].”
Recent developments on the construction of spatio-temporal covariance models (2008, background): “See, for instance, Handcock and Wallis (1994), Onof et al. (1996), Christakos and Hristopulos (1998), Haas (1998), Meiring et al. (1998), Northrop (1998), Wikle et al. (1998) , Cressie and Huang (1999), Wikle and Cressie (1999), Christakos (2000), Knorr-Held (2000), Wheater et al. (2000), Brown et ”
Computationally Efficient Convolved Multiple Output Gaussian Processes (2011, background): “, 1998; Fuentes, 2002a,b; Paciorek and Schervish, 2004) and spatiotemporal covariances (Wikle et al., 1998; Wikle, 2002, 2003).”
Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations (2009, unknown): “Another advantage with our approach is its generality, which makes it possible to perform Bayesian analysis in an automatic, streamlined way, and to compute model comparison criteria and various predictive measures so that models can be compared and the model under study can be challenged.”
A kernel-based spectral model for non-Gaussian spatio-temporal processes (2002, methodology): “…and Journel, 1999), multivariate time series approaches (Bennett, 1979), space–time dynamic model approaches (Guttorp et al., 1994; Mardia et al., 1998; Meiring et al., 1998; Wikle and Cressie, 1999), and hierarchical approaches (Wikle et al., 1998; Berliner et al., 2000; Wikle et al., 2001).”
Understanding movement data and movement processes: current and emerging directions. (2008, background): “This inference comes by factoring high dimensional problems into lower dimensional, conditionally dependent ones (Berliner 1996; Wikle et al. 1998; Clark 2005).”
Hierarchical Bayesian Models for Predicting The Spread of Ecological Processes (2003, methodology): “For complicated spatio-temporal applications of these methods, see Wikle et al. (1998), Berliner et al. (2000), and Wikle et al. (2001).”
Multiresolution Markov models for signal and image processing (2002, background): “, a first- or higher-order MRF on a grid such as Figure 14), and to [342, 147] for approaches to direct temporal propagation of a MR tree model for a space-time process.”
A Bayesian hierarchical approach to regional frequency analysis (2011, result): “This framework builds on previous work by several authors [in particular, Wikle et al., 1998; Perreault, 2000; Micevski et al., 2006; Micevski, 2007; Cooley et al., 2007; Lima and Lall, 2009, 2010; Aryal et al., 2009], who explored the usefulness of spatial and temporal Bayesian hierarchical models.”
EOF Calculations and Data Filling from Incomplete Oceanographic Datasets (2003, methodology): “…autocorrelations can be considered by using multivariate ARIMA and space-time autoregressive models (Kamarianakis and Prastacos, 2003;20 Wikle et al., 1998; Kamarianakis and Prastacos, 2005); however, ARIMA cannot capture nonlinear trends because it assumes a linear dependence between…”