Statistics For Spatial Data

By L. Vogt. 2016

3,209 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b5940f8ceaf3c43d084d821f402cf4f868cabd08

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Citations in the corpus

Opening the black box: an open-source release of Maxent (2017, methodology): “An IPP is a widely-used model for a random set Z of points falling in some domain D (Cressie 1993, Diggle 2003).”
When Should You Adjust Standard Errors for Clustering? (2017, unknown): “, Liang and Zeger [1986], Diggle et al. [2013], Bertrand et al.”
Spatial weights matrix selection and model averaging for spatial autoregressive models (2017, background): “1 Early development in estimation and testing can be found in Anselin (1988, 1992), Kelejian and Robinson (1993), Cressie (1993), Anselin and Florax (1995), Anselin and Rey (1997), and Anselin and Bera (1998), among others.”
Integrated species distribution models: combining presence‐background data and site‐occupancy data with imperfect detection (2017, background): “In addition, note that whereas the IPP model is formulated on continuous space, the conventional SO model is applied to a set of discrete sites in space.”
Covariance Regression Analysis (2017, unknown): “Accordingly, spatial statistics attempts to explain the covariance structure of responses by their geographical locations (Cressie 1991; Bivand, Pebesma, and Gómez-Rubio 2008; Cressie and Wikle 2011).”
Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables (2018, background): “Most geostatistical models assume that the target variable Y at some geographic location s can be modeled as the sum of a deterministic mean (μ) and a stochastic residual (ε) (Goovaerts, 1997; Cressie, 2015): Y (s)=μ(s)+ε(s).”
Non-Denoising Forward-Time Diffusions (2023, methodology): “We rely on the semivariogram approach (Cressie, 1993) to compare how different covariance functions fit D ( CIFAR ) .”
Positive biodiversity-productivity relationship predominant in global forests (2016, methodology): “Because GFB ground plots represent approximately 40 percent of the forested areas, we used universal kriging (62) to estimate P and q for the areas with no GFB sample coverage.”
Interpolation in Time Series : An Introductive Overview of Existing Methods, Their Performance Criteria and Uncertainty Assessment (2017, methodology): “…other kriging methods that are used for spatial (multivariate) and time (univariate) interpolation: log-normal kriging [65], trans-Gaussian kriging [66], factorial kriging, [67] and variability decomposition in combination with kriging with a relative variogram and non-stationary residual variance…”
GSpartan: a Geospatio-Temporal Multi-task Learning Framework for Multi-location Prediction (2016, background): “Variogram is a measure developed in spatial statistics to determine the spatial dependence between a pair of locations [10].”
Performance evaluation and hyperparameter tuning of statistical and machine-learning models using spatial data (2018, background): “Otherwise, the independence assumption might be violated which in turn might lead to biased coefficients and p-values and hence wrong (ecological) conclusions (Cressie, 1993; Dormann et al., 2007; Telford & Birks, 2005).”
Geostatistical Modelling Using Non‐Gaussian Matérn Fields (2015, background): “Furthermore, the posterior variance of the process in the same scale as the data is given by VŒX.s/2 j y D 2VŒX.s/ j y 2 C 4EŒX.s/ j y 2VŒX.s/ j y : Hence, the observations y and the mean field affect the Kriging variance for the transformed Gaussian model, through the term EŒX.s/ j y .”
Disaggregating daily precipitations into hourly values with a transformed censored latent Gaussian process (2015, unknown): “The minimum of the trace of the Godambe matrix is sought starting with a classical weighted least square estimation g WLS based on the empirical variogram, as advocated in Cressie (1993) and Chile`s and Delﬁner (2012).”
Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure (2017, methodology): “…regression models with independent random error assumptions to more complex model structures such as conditional spatial autoregressive models (CAR; Cressie 1993), integrated nested Laplace approximations (INLA; Rue et al. 2009), or geographically weighted regressions (GWR; Fotheringham et al.…”
Comparing implementations of global and local indicators of spatial association (2018, unknown): “Other authors have built on this work, notably Cliff and Ord (1969, 1973, 1981), and it is this development of a more general test statistic that is covered by Ripley (1981), Goodchild (1986) and Cressie (1993).”
Geostatistical prediction of flow–duration curves in an index-flow framework (2014, methodology): “the catchment drainage area) ( Cressie, 1993; Skøien et al. , 2006); this implies that the kriging system (Eq.”