Handbook of spatial statistics

By Alan E. Gelfand, Peter J. Diggle, Peter Guttorp, Montserrat Fuentes. 2010

505 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Environmental Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/1b7bbd456b65c2cb3f31fbcb156dbefbc3847828

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Citations in the corpus

Spatio-temporal modeling of particulate matter concentration through the SPDE approach (2012, methodology): “In the geostatistical approach (see for example Cressie 1993; Cressie and Wikle 2011; Gelfand et al 2010), data coming from monitoring net works are assumed to be realizations of a continuously indexed spatial process ( random ﬁeld ) changing in time denoted by Y ( s , t ) ≡ { y ( s , t ) : ( s ,…”
Gaussian Processes for history-matching: application to an unconventional gas reservoir (2017, background): “More importantly, as in our work, the spherical covariance function does not guarantee positive-definiteness anymore above three dimensions [137, 138].”
Spatiotemporal Learning via Infinite-Dimensional Bayesian Filtering and Smoothing: A Look at Gaussian Process Regression Through Kalman Filtering (2013, background): “S PATIO-temporal Gaussian processes, or Gaussian ﬁelds, arise in many disciplines such as spatial statistics and kriging, machine learning, physical inverse problems, and signal processing [1], [2], [3], [4], [5].”
Assessment of Point Process Models for Earthquake Forecasting (2013, background): “Other recent examples, such as the use of functional summaries in a study of infectious disease, can be found in Gelfand et al. (2010).”
Changes in Spatiotemporal Precipitation Patterns in Changing Climate Conditions (2016, methodology): “We use for this purpose a nonparametric kernel estimator, a standard procedure in spatial point process analysis (see, e.g., Gelfand et al. 2010, chapter 21).”
Dismantling the Mantel tests (2011, background): “This is widely considered as a parsimonious, yet flexible and powerful model to study variables structured in space (Chil es & Delfiner 1999; Lantu ejoul 2002; Diggle, Ribeiro & Christensen 2003; Wackernagel 2003; Gelfand et al. 2010).”
A non-model-based approach to bandwidth selection for kernel estimators of spatial intensity functions (2018, background): “Typical examples include the map of trees in a forest stand, the addresses of individuals infected with some disease, and the locations of cells in a tissue (Diggle, 2014; Gelfand et al., 2010; Illian et al., 2008).”
Social Networks and the Identification of Peer Effects (2013, background): “The same issues arise in spatial statistics, where this choice between infill and domain-increasing asymptotics is also important (see, e.g., Schabenberger and Gotway 2004; Gelfand et al. 2010).”
Functional peaks‐over‐threshold analysis (2020, background): “Gnedenko, B. (1943). Sur la Distribution Limite du Terme Maximum d’une Série Aléatoire. Annals of Mathematics, 44(3):423–453. Gneiting, T. and Raftery, A. E. (2007). Strictly Proper Scoring Rules, Prediction, and Estimation. Journal of the American Statistical Association, 102(477):359– 378. Hall, T”