Geostatistical inference under preferential sampling

By Peter J. Diggle, Raquel Menezes, Ting-Li Su. 2010

403 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Environmental Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/9621d7d6ddd07399de4b2fdef8ba5a94e805336b

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Citations in the corpus

Inhibitory geostatistical designs for spatial prediction taking account of uncertain covariance structure (2016, methodology): “have often been criticised because they leave large unsampled swaths in the study region D (Müller, 2007). Studies of design for efficient spatial prediction with known covariance structure include McBratney et al. (1981); McBratney and Webster (1981); Yfantis et al. (1987); Ritter (1996); Su and Ca”
A review of machine learning applications in wildfire science and management (2020, background): “A form of spatial selection bias called preferential sampling occurs when sampling occurs preferentially in locations where one expects a certain response [Diggle et al., 2010].”
Bayesian computing with INLA: New features (2012, background): “…can be found in Guo and Carlin (2004) where longitudinal and event time data are jointly modeled or in the preferential sampling framework of Diggle et al. (2010) where geostatistical models with stochastic dependence between the continuous measurements and the locations at which the…”
Spatiotemporal wildfire modeling through point processes with moderate and extreme marks (2021, background): “Models for preferentially sampled spatial data (Diggle et al. 2010) can be viewed as marked point processes with shared effects, such that our approach would allow for capturing preferential sampling effects specifically in extreme values.”
Spatial sampling design and covariance-robust minimax prediction based on convex design ideas (2010, background): “Works falling into none of the categories mentioned up to now are Bueso et al. (1999), Angulo and Bueso (2001), Chang et al. (2005) and Diggle et al. (2009) : Bueso and Angulo (1999) present spatial sampling design, when the variable of interest cannot be directly observed, but information on it ca”