Statistics for Spatio-Temporal Data

By Christian P. Robert. 2014

1,419 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Environmental Engineering

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Citations in the corpus

Differentiable Physics-informed Graph Networks (2019, background): “Cressie & Wikle (2015) covered a number of statistical models handling physical equations.”
A General Framework for Vecchia Approximations of Gaussian Processes (2017, background): “Gaussian processes (GPs) have become popular choices as models or prior distributions for functions, time series, and spatial ﬁelds (e.g., Banerjee et al., 2004; Rasmussen and Williams, 2006; Cressie and Wikle, 2011).”
Spatial interpolation methods applied in the environmental sciences: A review (2014, background): “Since the publication of the review of geostatistical space-time models (Kyriakidis and Journel, 1999), there are rapid developments in statistics for spatio-temporal data (Banerjee et al., 2004; Cressie and Wikle, 2011).”
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 ,…”
A penalized regression model for spatial functional data with application to the analysis of the production of waste in Venice province (2016, methodology): “When analyzing the temporal evolutions of the amount of per capita municipal waste, we shall make a strong simplification of the nature of these data, and consider them in the framework of geostatistical functional data (Delicado et al. 2010), where the datum is observable in principle in any point ”
Hierarchical statistical modeling of big spatial datasets using the exponential family of distributions (2013, methodology): “The hierarchical modeling framework defined above yields a spatial version of the GLM framework; it was proposed by Diggle et al. (1998), who assumed a Gaussian model for Y (·) and a prior distribution on its parameters.”
Model‐based inference for estimating shifts in species distribution, area occupied and centre of gravity (2016, background): “…density r(s, t) of the species when encountered: dðs; tÞ ¼ pðs; tÞrðs; tÞ: We specify that each of these processes follows a first-order random walk process (Cressie & Wikle 2011), that is encounter probability p(s, t) and expected density when encountered r(s, t) in year t depend only upon…”
Bayesian inference for palaeoclimate with time uncertainty and stochastic volatility (2015, methodology): “We structure our model similarly to that defined by Berliner (1996), though similar concepts are found in state space models (e.g. Cressie and Wikle (2011)) and dynamic linear models (West and Harrison, 1999).”
Multi-scale Graphical Models for Spatio-Temporal Processes (2014, background): “Other examples of multi-scale spatio-temporal phenomena with the network structure include: flow of information through neural/brain networks [15], traffic flow through traffic networks[3]; spread of memes through social networks [23]; diffusion of salinity, temperature, pressure and pollutants in a”
Estimation of a non-stationary model for annual precipitation in southern Norway using replicates of the spatial field (2014, methodology): “Gaussian random fields (GRFs) play an important role in spatial statistics; they are commonly used to model the spatial process itself, or as building blocks in hierarchical models (see e.g. Banerjee et al., 2004; Cressie and Wikle, 2011).”
Nested Sequential Monte Carlo Methods (2015, neutral): “One example of the type of models we consider are the so-called spatio-temporal models (Wikle, 2015; Cressie & Wikle, 2011; Rue & Held, 2005).”
History and trends in solar irradiance and PV power forecasting: A preliminary assessment and review using text mining (2018, methodology): “In the field of spatio-temporal statistics, spatiotemporal kriging, which uses a covariance function to describe the spatio-temporal process, is considered to be a major method for spatio-temporal prediction (Cressie and Wikle, 2011).”
Statistical solutions for error and bias in global citizen science datasets (2014, background): “Given the importance of spatial and temporal processes in 444 ecology, an increasing body of literature is available describing how to deal 445 with the spatial and temporal distribution of data (Cressie and Wikle, 2011, 446 Zhang 2012).”
Small Area Estimation via Multivariate Fay–Herriot Models with Latent Spatial Dependence (2013, methodology): “As summarized in Cressie & Wikle (2011), there are several approaches to modelling multivariate spatial structure, but the one that has gained the most favour in recent years is the conditional approach first detailed in a hierarchical framework by Royle & Berliner (1999).”
Random Forests for dependent data (2020, neutral): “Similarly, in geostatistics, Gaussian Processes (GP) [5, 13, 57] are widely used to model the dependent error process (`).”