Bayesian modelling of inseparable space-time variation in disease risk.
By L. Knorr‐Held. Statistics in Medicine, 2000
677 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Epidemiology, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/1e730b88655db6874277ea876765a95c6de8fe1a
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In Economics and Econometrics
- Finn Lindgren
- Håvard Rue
- Daniel P. Simpson
- David Bolin
- Andrea Riebler
- Geir-Arne Fuglstad
- Michela Cameletti
- H. Bakka
In Epidemiology
- Daniel P. Simpson
- Michela Cameletti
- Gianluca Baio
- Håvard Rue
- Marta Blangiardo
- David Bolin
- Finn Lindgren
- Elias Teixeira Krainski
In Statistics and Probability
- Andrea Riebler
- Daniel P. Simpson
- Finn Lindgren
- Håvard Rue
- H. Bakka
- David Bolin
- Geir-Arne Fuglstad
- Michela Cameletti
Citations in the corpus
- Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations (2009, neutral): “Allcroft and Glasbey (2003); Carlin and Banerjee (2003); Knorr-Held (2000) ; Knorr-Held and Besag (1998) and Wikle et al. (1998).”
- Recent developments on the construction of spatio-temporal covariance models (2008, background): “See, for instance, Handcock and Wallis (1994), Onof et al. (1996), Christakos and Hristopulos (1998), Haas (1998), Meiring et al. (1998), Northrop (1998), Wikle et al. (1998), Cressie and Huang (1999), Wikle and Cressie (1999), Christakos (2000), Knorr-Held (2000) , Wheater et al. (2000), Brown et ”
- Spatial and spatio-temporal models with R-INLA. (2013, methodology): “The assumption of linearity in the δ i can be released (Knorr-Held, 2000) using a dynamic non parametric formulation for the linear predictor Here α , υ i and ν i have the same parametrisation as in (6); however, the term γ t represents the temporally structured effect, modelled dynamically (e.g.…”
- Spatio-temporal stationary covariance models (2003, methodology): “The second, used in Markov random field modeling of spatio-temporal data, is based on precision matrices (see [10] and references therein).”
- Spatial modelling with R-INLA: A review (2018, methodology): “els Extending spatial model components to space-time interaction components is straightforward using R-INLA, see Section 2, and thus the Besag model can be extended to a space-time interaction model. Knorr-Held (2000) proposed four interaction types where the structure matrix results as a Kronecker ”
- Spatial and spatio-temporal models with R-INLA. (2013, neutral): “Consequently, we assume no spatial and/or temporal structure on the interaction and therefore δit ∼ Normal(0, τδ) — see Knorr-Held (2000) for a detailed description of other specifications.”