Modern Spatiotemporal Geostatistics

By George Christakos. 2000

508 citations recorded by the source corpus.

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Citations in the corpus

On digital soil mapping (2003, background): “Bayesian maximum entropy ( BME ) was intro - duced by Christakos ( 1990 , 2000 ) .”
Classes of Kernels for Machine Learning: A Statistics Perspective (2002, background): “Cressie and Huang (1999) and Gneiting (2002b) use (10) to derive nonseparable space-time stationary kernels, see also Christakos (2000) for illustrative examples.”
Spatio-temporal stationary covariance models (2003, background): “Many other practical examples can be found in [4,5] and references therein.”
Space-time mapping of soil salinity using probabilistic bayesian maximum entropy (2004, background): “Kriging is, in what follows, described briefly (Christakos, 2000).”
Maximum likelihood Bayesian averaging of uncertain model predictions (2003, background): “(1999) note that Draper (1995), Raftery et al. (1996) and Volinsky et al. (1997) have shown this to be useful in the BMA context.”
Some new random field tools for spatial analysis (2008, methodology): “orne contaminants, with a theory as described in [7]. Geostatistics and other earth sciences, where, as for oceanography, the spatial aspects of parameter spaces may be two or three dimensional. cf. [5,6,11]. Astrophysics, where random ﬁeld techniques have been heavily used in analysing the COBE (Co”
Methods for generating non-separable spatiotemporal covariance models with potential environmental applications (2004, background): “Several theoretical models of non-separable covariances can be found: (a) in the spatiotemporal stochastics literature [2,3,7–15,19,37]; as well as (b) in the statistics literature [16,18,20,27–30,33,36].”
Geostatistical Space-Time Models, Stationarity, Separability, and Full Symmetry (2007, background): “These and related approaches have been discussed by Jones and Zhang (1997), Christakos (2000), Brown, K̊aresen, Roberts and Tonellato (2000), Kovolos, Christakos, Hristopulos and Serre (2004), Stein (2004, 2005) and Jun and Stein (2004).”
On spatial prediction of soil properties in the presence of a spatial trend: the empirical best linear unbiased predictor (E‐BLUP) with REML (2006, result): “This rationale is in concordance with other recent developments in geostatistics (Christakos, 2000).”
Nonseparable, Stationary Covariance Functions for Space–Time Data (2002, background): “Christakos (2000, p. 18), for example, argues that “in modern spatiotemporal geostatistics, the rational approach for choosing the appropriate model from the data is by means of a theory that represents the physical knowledge available.” Dynamic geophysical processes such as wind patterns or ocean c”
Space–Time Covariance Functions (2005, background): “Christakos (1992, 2000), Jones and Zhang (1997), Cressie and Huang (1999), Brown, Karesen, Roberts, and Tonellato (2000), de Iaco, Myers, and Posa (2001, 2002, 2003), Gneiting (2002), Ma (2003), and Hartfield and Gunst (2003) described some recent efforts to develop new classes of stationary space–…”
Statistical methods for regular monitoring data (2005, methodology): “Why use these models rather than any of the other space–time models that have been suggested in recent works such as Christakos (1992, 2000), Jones and Zhang (1997), Cressie and Huang (1999), Brown et al. (2000), de Iaco et al. (2001, 2002, 2003), Gneiting (2002), Hartfield and Gunst (2003), Ma…”
Soil salinity mapping using spatio-temporal kriging and Bayesian maximum entropy with interval soft data (2005, methodology): “The BME approach, promoted by Christakos (1990, 2000), provides a systematic and rigorous way to incorporate soft data and other sources of information, in addition to hard data, into the analysis.”
Bayesian Maximum Entropy prediction of soil categories using a traditional soil map as soft information (2008, background): “Bayesian Maximum Entropy (BME) is a relatively new approach for spatialmapping that allows the user to incorporate a wide variety of data sources of various quality on a sound theoretical basis (Christakos, 2000; Christakos et al., 2002).”