Spatio-temporal modeling of particulate matter concentration through the SPDE approach

By Michela Cameletti, Finn Lindgren, Daniel P. Simpson, Håvard Rue. AStA Advances in Statistical Analysis, 2012

406 citations recorded by the source corpus.

Subfields: Automotive Engineering, Environmental Engineering, Health, Toxicology and Mutagenesis

View Paper: https://www.semanticscholar.org/paper/c1960b334e047740debcb23d162c6e8355d203a8

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Citations in the corpus

Bayesian Spatial Modelling with R-INLA (2015, methodology): “…where each basis function is the product of a spatial and a temporal basis function, generates a precision matrix for the weight vector et al. (2013) for a case-study using a Kronecker-model based on a temporal AR(1) process, including the full R code (although note that the interface has…”
Geostatistical Modelling Using Non‐Gaussian Matérn Fields (2015, background): “…is commonly referred to as trans-Gaussian Kriging (Cressie, 1993) and common transformations include the square-root transform (Cressie, 1993; Huerta et al., 2004; Berrocal et al., 2010; Sahu & Mardia, 2005) and the log transform (Cressie, 1993; Cameletti et al., 2013; Bolin & Lindgren, 2011).”
Real-Time Profiling of Fine-Grained Air Quality Index Distribution Using UAV Sensing (2017, methodology): “ via different PDTs. 1) Model Accuracy: In Fig. 7, we compare three prediction models, our regression model GPM-NN, linear interpolation (LI) [39] and classical multi-variable linear regression (MLR) [26], respectively, versus different values of PDT. LI uses interpolation to estimate the AQI value ”
Bayesian computing with INLA: New features (2012, methodology): “At each time the points are spatially correlated while across the time periods, they evolve according to an AR(1) process; see for example Cameletti et al. (2012).”
Spatial models with explanatory variables in the dependence structure (2014, background): “It was introduced by Lindgren et al. in 2011 and has since been extended and applied in various contexts (Bolin and Lindgren, 2011, 2013; Simpson et al., 2012a,b; Cameletti et al., 2013).”
Probabilistic Interpolation of Uncertain Local Activation Times on Human Atrial Manifolds (2020, methodology): “where QT is the precision matrix of an AR(1) process with correlation parameter ρ; see [23], [25] for more detail.”
Excursion and contour uncertainty regions for latent Gaussian models (2012, background): “A typical example is in studies of air pollution, where the interest is in testing whether, and where, the level of pollution exceeds some given limit value set by a regulatory agency (Cameletti et al., 2013), and similar examples can be found in a wide range of scientific fields including brain ima”
Spatial and spatio-temporal models with R-INLA. (2013, methodology): “…a model is widely used in the air quality literature thanks to its ﬂexibility in modeling the eﬀect of relevant covariates (i.e. meteorological and geographical variables) as well as time and space dependence (e.g. Cocchi et al., 2007; Cameletti et al., 2011a; Sahu, 2012; Fass`o and Finazzi, 2011).”