Estimation of Disease Rates in Small Areas: A new Mixed Model for Spatial Dependence

By B. Leroux, Xingye Lei, N. Breslow. 2000

691 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/31f8be74a8b28812450bccd28de4c9c6e2a8fabe

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Citations in the corpus

Small Area Estimation via Multivariate Fay–Herriot Models with Latent Spatial Dependence (2013, methodology): “ areas. The use of spatial dependence structures in SAE is not new, with models utilizing conditional autoregressive (CAR) spatial structure that is prevalent in the disease mapping literature (e.g., Leroux et al., 1999; MacNab, 2003), which builds upon the classic work of Clayton and Kaldor (1987),”
Modelling spatially correlated data via mixtures: a Bayesian approach (2002, background): “The term Σni=1x 2 i in the exponent in expression (3.1) does not appear in the conditional autoregressive model, as in, for example, Besag et al. (1991), although it does in the formulation proposed recently by Leroux et al. (2000).”
Spatial modelling with R-INLA: A review (2018, methodology): “Discretely indexed models where the precision matrix depends on hyper-parameters can be implemented using rgeneric , such as the model proposed by Dean et al. (2001), and the Leroux model (Leroux et al., 2000).”
Penalising Model Component Complexity: A Principled, Practical Approach to Constructing Priors (2014, background): “roposed by Dean et al. (2001). However, they did not assume a scaled structured spatial effect, which is essential for controlling the inﬂuence of the corresponding hyperprior (Sørbye and Rue, 2014). Leroux et al. (2000) proposed a slightly different reparameterisation, which has been widely promote”
An intuitive Bayesian spatial model for disease mapping that accounts for scaling (2016, methodology): “The appropriateness and behaviour of different latent spatial models have been compared in a full Bayesian context, an empirical Bayes setting(10) or using penalised quasi likelihood.(4) It is accepted that the Besag model may lead to misleading results in the case where there is no spatial correlat”