Statistics for Spatio-Temporal Data

By Noel Cressie, Christopher K. Wikle. 2011

825 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/bc8cb930c10d7664fdfe548bbed6f2d5f9b48804

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Citations in the corpus

Physics constrained nonlinear regression models for time series (2012, methodology): “A central issue in contemporary science is the development of data driven statisticaldynamical models for the time series of a partial subset of observed variables, uI(t) ∈ R N1 , which arise from observations of nature or from an extremely complex physical model [1, 2, 3, 4, 5, 6, 7, 8, 9, 10].”
Is Gun Violence Contagious? A Spatiotemporal Test (2018, background): “K(s, t) is proportional to the expected number of additional points within a distance s and time window t of an arbitrary event (Cressie and Wikle, 2011, p. 210).”
Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets (2014, background): “…interest in statistical modeling for location-referenced spatial data; see, for example, the books by Stein (1999), Moller and Waagepetersen (2003), Schabenberger and Gotway (2004), and Cressie and Wikle (2011), and Banerjee, Carlin, and Gelfand (2014) for a variety of methods and applications.”
Spatial semiparametric models improve estimates of species abundance and distribution (2014, methodology): “isotropic exponential correlation model (Cressie and Wikle 2011).”
Hierarchical Species Distribution Models (2016, methodology): “A statistical model describing the pattern created by points in space and time is the space-time Poisson point process distribution [16, 20].”
Towards Causal Inference for Spatio-Temporal Data: Conflict and Forest Loss in Colombia (2020, background): “5.08639v2 [stat.ME] 18 Jun 2020 information can be exploited for statistical modeling in various ways; this is the study of spatio-temporal statistics [see, e.g., Sherman, 2011, Montero et al., 2015, Cressie and Wikle, 2015, Wikle et al., 2019]. Spatio-temporal statistical models nd their applicatio”
Computational microstructure characterization and reconstruction: Review of the state-of-the-art techniques (2018, methodology): “Similarly, in geosciences, variogram-based [276] and parametric [277] approaches have also previously been used to model (spatial) correlations in data.”
Count Time Series: A Methodological Review (2021, background): “See Berliner (1996) and Cressie and Wikle (2011) for broad discussions of this paradigm.”
A Multiresolution Gaussian Process Model for the Analysis of Large Spatial Datasets (2015, background): “More recently the breakthroughs in Bayesian hierarchical models have added rich new classes of models for handling heterogenous spatial data and indirect measurements of spatial processes (Banerjee et al. 2003; Cressie and Wikle 2011).”